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Frings M, Welsner M, Mousa C, Zensen S, Salhöfer L, Meetschen M, Beck N, Bos D, Westhölter D, Wienker J, Taube C, Umutlu L, Schaarschmidt BM, Forsting M, Haubold J, Sutharsan S, Opitz M. Low-dose high-resolution chest CT in adults with cystic fibrosis: intraindividual comparison between photon-counting and energy-integrating detector CT. Eur Radiol Exp 2024; 8:105. [PMID: 39298080 DOI: 10.1186/s41747-024-00502-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 08/02/2024] [Indexed: 09/21/2024] Open
Abstract
BACKGROUND Regular disease monitoring with low-dose high-resolution (LD-HR) computed tomography (CT) scans is necessary for the clinical management of people with cystic fibrosis (pwCF). The aim of this study was to compare the image quality and radiation dose of LD-HR protocols between photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) in pwCF. METHODS This retrospective study included 23 pwCF undergoing LD-HR chest CT with PCCT who had previously undergone LD-HR chest CT with EID-CT. An intraindividual comparison of radiation dose and image quality was conducted. The study measured the dose-length product, volumetric CT dose index, effective dose and signal-to-noise ratio (SNR). Three blinded radiologists assessed the overall image quality, image sharpness, and image noise using a 5-point Likert scale ranging from 1 (deficient) to 5 (very good) for image quality and image sharpness and from 1 (very high) to 5 (very low) for image noise. RESULTS PCCT used approximately 42% less radiation dose than EID-CT (median effective dose 0.54 versus 0.93 mSv, p < 0.001). PCCT was consistently rated higher than EID-CT for overall image quality and image sharpness. Additionally, image noise was lower with PCCT compared to EID-CT. The average SNR of the lung parenchyma was lower with PCCT compared to EID-CT (p < 0.001). CONCLUSION In pwCF, LD-HR chest CT protocols using PCCT scans provided significantly better image quality and reduced radiation exposure compared to EID-CT. RELEVANCE STATEMENT In pwCF, regular follow-up could be performed through photon-counting CT instead of EID-CT, with substantial advantages in terms of both lower radiation exposure and increased image quality. KEY POINTS Photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) were compared in 23 people with cystic fibrosis (pwCF). Image quality was rated higher for PCCT than for EID-CT. PCCT used approximately 42% less radiation dose and offered superior image quality than EID-CT.
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Affiliation(s)
- Marko Frings
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
| | - Matthias Welsner
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Christin Mousa
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Luca Salhöfer
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Mathias Meetschen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Nikolas Beck
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Dirk Westhölter
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Johannes Wienker
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Christian Taube
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sivagurunathan Sutharsan
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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Devkota S, Garg M, Debi U, Dhooria S, Dua A, Prabhakar N, Soni S, Maralakunte M, Gulati A, Singh T, Sandhu MS. Evaluating Lung Changes in Long COVID: Ultra-Low-Dose vs. Standard-Dose CT Chest. Br J Biomed Sci 2024; 81:13385. [PMID: 39319349 PMCID: PMC11420527 DOI: 10.3389/bjbs.2024.13385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 08/13/2024] [Indexed: 09/26/2024]
Abstract
Background Frequent chest CTs within a short period during follow-up of long COVID patients may increase the risk of radiation-related health effects in the exposed individuals. We aimed to assess the image quality and diagnostic accuracy of ultra-low-dose CT (ULDCT) chest compared to standard-dose CT (SDCT) in detecting lung abnormalities associated with long COVID. Methods In this prospective study, 100 long COVID patients with respiratory dysfunction underwent SDCT and ULDCT chest that were compared in terms of objective (signal-to-noise ratio, SNR) and subjective image quality (image graininess, sharpness, artifacts, and diagnostic accuracy along with the European guidelines on image quality criteria for CT chest), detection of imaging patterns of long COVID, CT severity score, and effective radiation dose. Additionally, the diagnostic performance of ULDCT was compared among obese (BMI≥30 kg/m2) and non-obese (BMI<30 kg/m2) subjects. Results The mean age of study participants was 53 ± 12.9 years, and 68% were male. The mean SNR was 31.4 ± 5.5 and 11.3 ± 4.6 for SDCT and ULDCT respectively (p< 0.0001). Common findings seen on SDCT included ground-glass opacities (GGOs, 77%), septal thickening/reticulations (67%), atelectatic/parenchymal bands (63%) and nodules (26%). ULDCT provided sharp images, with no/minimal graininess, and high diagnostic confidence in 81%, 82% and 80% of the cases respectively. The sensitivity of ULDCT for various patterns of long COVID was 72.7% (GGOs), 71.6% (interlobular septal thickening/reticulations), 100% (consolidation), 81% (atelectatic/parenchymal bands) and 76.9% (nodules). ULDCT scans in non-obese subjects exhibited a significantly higher sensitivity (88% vs. 60.3%, p < 0.0001) and diagnostic accuracy (97.7% vs. 84.9%, p < 0.0001) compared to obese subjects. ULDCT showed very strong correlation with SDCT in terms of CT severity score (r = 0.996, p < 0.0001). The mean effective radiation dose with ULDCT was 0.25 ± 0.02 mSv with net radiation dose reduction of 94.8% ± 1.7% (p < 0.0001) when compared to SDCT (5.5 ± 1.96 mSv). Conclusion ULDCT scans achieved comparable diagnostic accuracy to SDCT for detecting long COVID lung abnormalities in non-obese patients, while significantly reducing radiation exposure.
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Affiliation(s)
- Shritik Devkota
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Mandeep Garg
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Uma Debi
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Sahajal Dhooria
- Department of Pulmonary Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Ashish Dua
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Nidhi Prabhakar
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Saumya Soni
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Muniraju Maralakunte
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Ajay Gulati
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Tarvinder Singh
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Manavjit Singh Sandhu
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
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Bae H, Lee JW, Jeong YJ, Hwang MH, Lee G. Increased Scan Speed and Pitch on Ultra-Low-Dose Chest CT: Effect on Nodule Volumetry and Image Quality. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1301. [PMID: 39202582 PMCID: PMC11356370 DOI: 10.3390/medicina60081301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 09/03/2024]
Abstract
Background and Objectives: This study's objective was to investigate the influence of increased scan speed and pitch on image quality and nodule volumetry in patients who underwent ultra-low-dose chest computed tomography (CT). Material and Methods: One hundred and two patients who had lung nodules were included in this study. Standard-speed, standard-pitch (SSSP) ultra-low-dose CT and high-speed, high-pitch (HSHP) ultra-low-dose CT were obtained for all patients. Image noise was measured as the standard deviation of attenuation. One hundred and sixty-three nodules were identified and classified according to location, volume, and nodule type. Volume measurement of detected pulmonary nodules was compared according to nodule location, volume, and nodule type. Motion artifacts at the right middle lobe, the lingular segment, and both lower lobes near the lung bases were evaluated. Subjective image quality analysis was also performed. Results: The HSHP CT scan demonstrated decreased motion artifacts at the left upper lobe lingular segment and left lower lobe compared to the SSSP CT scan (p < 0.001). The image noise was higher and the radiation dose was lower in the HSHP scan (p < 0.001). According to the nodule type, the absolute relative volume difference was significantly higher in ground glass opacity nodules compared with those of part-solid and solid nodules (p < 0.001). Conclusion: Our study results suggest that HSHP ultra-low-dose chest CT scans provide decreased motion artifacts and lower radiation doses compared to SSSP ultra-low-dose chest CT. However, lung nodule volumetry should be performed with caution for ground glass opacity nodules.
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Affiliation(s)
- Heejoo Bae
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Republic of Korea (J.W.L.); (M.-H.H.)
| | - Ji Won Lee
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Republic of Korea (J.W.L.); (M.-H.H.)
| | - Yeon Joo Jeong
- Department of Radiology and Medical Research Institute, Yangsan Pusan National University Hospital, Pusan National University School of Medicine, Busan 50612, Republic of Korea;
| | - Min-Hee Hwang
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Republic of Korea (J.W.L.); (M.-H.H.)
| | - Geewon Lee
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Republic of Korea (J.W.L.); (M.-H.H.)
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Kimura Y, Suyama TQ, Shimamura Y, Suzuki J, Watanabe M, Igei H, Otera Y, Kaneko T, Suzukawa M, Matsui H, Kudo H. Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm. Radiol Phys Technol 2024; 17:367-374. [PMID: 38413510 DOI: 10.1007/s12194-024-00786-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/11/2024] [Accepted: 01/25/2024] [Indexed: 02/29/2024]
Abstract
This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.
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Affiliation(s)
- Yuya Kimura
- Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan.
- Department of Clinical Epidemiology and Health Economics, School of Public Health, University of Tokyo, Tokyo, Japan.
| | - Takeru Q Suyama
- Nadogaya Research Institute, Nadogaya Hospital, Chiba, Japan
| | | | - Jun Suzuki
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
- Department of Radiology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Masato Watanabe
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Igei
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Yuya Otera
- Department of Radiology, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Takayuki Kaneko
- Radiological Physics and Technology Department, National Center for Global Health and Medicine, Tokyo, Japan
| | - Maho Suzukawa
- Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan
| | - Hirotoshi Matsui
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Kudo
- Institute of Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan
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Steuwe A, Kamp B, Afat S, Akinina A, Aludin S, Bas EG, Berger J, Bohrer E, Brose A, Büttner SM, Ehrengut C, Gerwing M, Grosu S, Gussew A, Güttler F, Heinrich A, Jiraskova P, Kloth C, Kottlors J, Kuennemann MD, Liska C, Lubina N, Manzke M, Meinel FG, Meyer HJ, Mittermeier A, Persigehl T, Schmill LP, Steinhardt M, The Racoon Study Group, Antoch G, Valentin B. Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project. Bioengineering (Basel) 2024; 11:207. [PMID: 38534481 DOI: 10.3390/bioengineering11030207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/28/2024] Open
Abstract
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.
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Affiliation(s)
- Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Benedikt Kamp
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Alena Akinina
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Schekeb Aludin
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Elif Gülsah Bas
- Department of Diagnostic and Interventional Radiology, University Hospital of Marburg, 35043 Marburg, Germany
| | - Josephine Berger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Evelyn Bohrer
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Alexander Brose
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, 48149 Münster, Germany
| | - Sergio Grosu
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander Gussew
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Felix Güttler
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Andreas Heinrich
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Petra Jiraskova
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | | | - Christian Liska
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Nora Lubina
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Mathias Manzke
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Felix G Meinel
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Andreas Mittermeier
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Lars-Patrick Schmill
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Manuel Steinhardt
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | | | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Birte Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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Feghali JA, Russo RA, Mamou A, Lorentz A, Cantarinha A, Bellin MF, Meyrignac O. Image quality assessment in low-dose COVID-19 chest CT examinations. Acta Radiol 2024; 65:3-13. [PMID: 36744376 PMCID: PMC9905706 DOI: 10.1177/02841851231153797] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 12/21/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Low-dose thoracic protocols were developed massively during the COVID-19 outbreak. PURPOSE To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols. MATERIAL AND METHODS COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models. RESULTS In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ. CONCLUSION Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.
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Affiliation(s)
- Joelle A Feghali
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Roberta A Russo
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Adel Mamou
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Axel Lorentz
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Alfredo Cantarinha
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Marie-France Bellin
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
- Faculty of Medicine, Paris-Saclay University, Le Kremlin-Bicêtre, France
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Olivier Meyrignac
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
- Faculty of Medicine, Paris-Saclay University, Le Kremlin-Bicêtre, France
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
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Ahmad IS, Li N, Wang T, Liu X, Dai J, Chan Y, Liu H, Zhu J, Kong W, Lu Z, Xie Y, Liang X. COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning. Bioengineering (Basel) 2023; 10:1314. [PMID: 38002438 PMCID: PMC10669345 DOI: 10.3390/bioengineering10111314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/28/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.
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Affiliation(s)
- Isah Salim Ahmad
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Na Li
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Tangsheng Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Xuan Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Yinping Chan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Haoyang Liu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Junming Zhu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Weibin Kong
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Zefeng Lu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
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8
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Hazem M, Ali SI, AlAlwan QM, Al Jabr IK, Alshehri SAF, AlAlwan MQ, Alsaeed MI, Aldawood M, Turkistani JA, Amin YA. Diagnostic Performance of the Radiological Society of North America Consensus Statement for Reporting COVID-19 Chest CT Findings: A Revisit. J Clin Med 2023; 12:5180. [PMID: 37629222 PMCID: PMC10455816 DOI: 10.3390/jcm12165180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/24/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that leads to variable degrees of illness, and which may be fatal. We evaluated the diagnostic performance of each chest computed tomography (CT) reporting category recommended by the Expert Consensus of the Radiological Society of North America (RSNA) in comparison with that of reverse transcription polymerase chain reaction (RT-PCR). We aimed to add an analysis of this form of reporting in the Middle East, as few studies have been performed there. Between July 2021 and February 2022, 184 patients with a mean age of 55.56 ± 16.71 years and probable COVID-19 infections were included in this retrospective study. Approximately 64.67% (119 patients) were male, while 35.33% (65 patients) were female. Within 7 days, all patients underwent CT and RT-PCR examinations. According to a statement by the RSNA, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each CT reporting category were calculated, and the RT-PCR results were used as a standard reference. The RT-PCR results confirmed a final diagnosis of COVID-19 infection in 60.33% of the patients. For COVID-19 diagnoses, the typical category (n = 88) had a sensitivity, specificity, PPV, and accuracy of 74.8%, 93.2%, 94.3%, and 92.5%, respectively. For non-COVID-19 diagnoses, the PPVs for the atypical (n = 22) and negative (n = 46) categories were 81.8% and 89.1%, respectively. The PPV for the indeterminate (n = 28) category was 67.9%, with a low sensitivity of 17.1%. However, the RSNA's four chest CT reporting categories provide a strong diagnostic foundation and are highly correlated with the RT-PCR results for the typical, atypical, and negative categories, but they are weaker for the indeterminate category.
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Affiliation(s)
- Mohammed Hazem
- Department of Surgery, Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; (I.K.A.J.); (S.A.F.A.)
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Sohag University, Sohag 82524, Egypt;
| | - Sayed Ibrahim Ali
- Department of Family and Community Medicine, Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; (S.I.A.); (J.A.T.)
- Educational Psychology Department, College of Education, Helwan University, Cairo 11795, Egypt
| | - Qasem M. AlAlwan
- Department of Radiology, King Fahd Hospital Hofuf, Al-Ahsa 36441, Saudi Arabia; (Q.M.A.); (M.Q.A.)
| | - Ibrahim Khalid Al Jabr
- Department of Surgery, Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; (I.K.A.J.); (S.A.F.A.)
| | - Sarah Abdulrahman F. Alshehri
- Department of Surgery, Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; (I.K.A.J.); (S.A.F.A.)
| | - Mohammed Q. AlAlwan
- Department of Radiology, King Fahd Hospital Hofuf, Al-Ahsa 36441, Saudi Arabia; (Q.M.A.); (M.Q.A.)
| | | | - Mohammed Aldawood
- Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia;
| | - Jamela A. Turkistani
- Department of Family and Community Medicine, Collage of Medicine, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; (S.I.A.); (J.A.T.)
| | - Yasser Abdelkarim Amin
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Sohag University, Sohag 82524, Egypt;
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9
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Maino C, Franco PN, Talei Franzesi C, Giandola T, Ragusi M, Corso R, Ippolito D. Role of Imaging in the Management of Patients with SARS-CoV-2 Lung Involvement Admitted to the Emergency Department: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13111856. [PMID: 37296708 DOI: 10.3390/diagnostics13111856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
During the waves of the coronavirus disease (COVID-19) pandemic, emergency departments were overflowing with patients suffering with suspected medical or surgical issues. In these settings, healthcare staff should be able to deal with different medical and surgical scenarios while protecting themselves against the risk of contamination. Various strategies were used to overcome the most critical issues and guarantee quick and efficient diagnostic and therapeutic charts. The use of saliva and nasopharyngeal swab Nucleic Acid Amplification Tests (NAAT) in the diagnosis of COVID-19 was one of the most adopted worldwide. However, NAAT results were slow to report and could sometimes create significant delays in patient management, especially during pandemic peaks. On these bases, radiology has played and continues to play an essential role in detecting COVID-19 patients and solving differential diagnosis between different medical conditions. This systematic review aims to summarize the role of radiology in the management of COVID-19 patients admitted to emergency departments by using chest X-rays (CXR), computed tomography (CT), lung ultrasounds (LUS), and artificial intelligence (AI).
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Affiliation(s)
- Cesare Maino
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Paolo Niccolò Franco
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Cammillo Talei Franzesi
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Teresa Giandola
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Maria Ragusi
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Rocco Corso
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
| | - Davide Ippolito
- Department of Diagnostic Radiology, IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
- School of Medicine, University of Milano Bicocca, Via Cadore 33, 20090 Monza, Italy
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10
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Suliman II, Khouqeer GA, Ahmed NA, Abuzaid MM, Sulieman A. Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose. Life (Basel) 2023; 13:life13040992. [PMID: 37109522 PMCID: PMC10146316 DOI: 10.3390/life13040992] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023] Open
Abstract
Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.
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Affiliation(s)
- Ibrahim I Suliman
- Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11642, Saudi Arabia
- Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11642, Saudi Arabia
| | - Ghada A Khouqeer
- Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11642, Saudi Arabia
| | - Nada A Ahmed
- Faculty of Science, Taibah University, Al Madinah Al Munawwarah 42353, Saudi Arabia
| | - Mohamed M Abuzaid
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Abdelmoneim Sulieman
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
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11
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Garg M, Devkota S, Prabhakar N, Debi U, Kaur M, Sehgal IS, Dhooria S, Bhalla A, Sandhu MS. Ultra-Low Dose CT Chest in Acute COVID-19 Pneumonia: A Pilot Study from India. Diagnostics (Basel) 2023; 13:diagnostics13030351. [PMID: 36766456 PMCID: PMC9914217 DOI: 10.3390/diagnostics13030351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
The rapid increase in the number of CT acquisitions during the COVID-19 pandemic raised concerns about increased radiation exposure to patients and the resultant radiation-induced health risks. It prompted researchers to explore newer CT techniques like ultra-low dose CT (ULDCT), which could improve patient safety. Our aim was to study the utility of ultra-low dose CT (ULDCT) chest in the evaluation of acute COVID-19 pneumonia with standard-dose CT (SDCT) chest as a reference standard. This was a prospective study approved by the institutional review board. 60 RT-PCR positive COVID-19 patients with valid indication for CT chest underwent SDCT and ULDCT. ULDCT and SDCT were compared in terms of objective (noise and signal-to-noise ratio) and subjective (noise, sharpness, artifacts and diagnostic confidence) image quality, various imaging patterns of COVID-19, CT severity score and effective radiation dose. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of ULDCT for detecting lung lesions were calculated by taking SDCT as a reference standard. The mean age of subjects was 47.2 ± 10.7 years, with 66.67% being men. 90% of ULDCT scans showed no/minimal noise and sharp images, while 93.33% had image quality of high diagnostic confidence. The major imaging findings detected by SDCT were GGOs (90%), consolidation (76.67%), septal thickening (60%), linear opacities (33.33%), crazy-paving pattern (33.33%), nodules (30%), pleural thickening (30%), lymphadenopathy (30%) and pleural effusion (23.33%). Sensitivity, specificity and diagnostic accuracy of ULDCT for detecting most of the imaging patterns were 100% (p < 0.001); except for GGOs (sensitivity: 92.59%, specificity: 100%, diagnostic accuracy: 93.33%), consolidation (sensitivity: 100%, specificity: 71.43%, diagnostic accuracy: 93.33%) and linear opacity (sensitivity: 90.00%, specificity: 100%, diagnostic accuracy: 96.67%). CT severity score (range: 15-25) showed 100% concordance on SDCT and ULDCT, while effective radiation dose was 4.93 ± 1.11 mSv and 0.26 ± 0.024 mSv, respectively. A dose reduction of 94.38 ± 1.7% was achieved with ULDCT. Compared to SDCT, ULDCT chest yielded images of reasonable and comparable diagnostic quality with the advantage of significantly reduced radiation dose; thus, it can be a good alternative to SDCT in the evaluation of COVID-19 pneumonia.
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Affiliation(s)
- Mandeep Garg
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
- Correspondence:
| | - Shritik Devkota
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Nidhi Prabhakar
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Uma Debi
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Maninder Kaur
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Inderpaul S. Sehgal
- Department of Pulmonary Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Sahajal Dhooria
- Department of Pulmonary Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Ashish Bhalla
- Department of Internal Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Manavjit Singh Sandhu
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
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12
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Wu AJ, Plodkowski A, Ginsberg M, Shin J, LaPlant Q, Shepherd A, Shaverdian N, Ng V, Gelblum D, Braunstein L, Rimner A. Detection of COVID-19 pulmonary manifestations with radiotherapy simulation CT imaging. Clin Imaging 2023; 93:83-85. [PMID: 36413878 PMCID: PMC9672689 DOI: 10.1016/j.clinimag.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/20/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
COVID-19 is associated with characteristic lung CT findings. Radiotherapy simulation CT scans may reveal characteristic COVID-19 findings and identify patients with active or prior infection. We reviewed patients undergoing CT simulation at a major cancer center in an early epicenter of the COVID-19 pandemic in the United States. Scans were reviewed by radiation oncologists using established radiographic criteria for COVID-19 pneumonia. Radiographic classifications were compared with available COVID-19 PCR test results. A one-tailed t-test was used to compare the rate of positive COVID-19 tests in radiographically suspicious vs. non-suspicious groups. Scans deemed suspicious were re-reviewed by expert diagnostic radiologists. 414 CT simulation scans were performed on 400 patients. 119 patients had COVID-19 PCR test results available. Radiation oncologists considered 71 scans (17.1%) suspicious for COVID-19. Of these, 23 had corresponding COVID-19 PCR tests, and 3/23 (15.7%) were positive for COVID. 107 non-suspicious scans had corresponding COVID-19 test results, and 9 were positive (8.4%). The difference in positive test results between suspicious and non-suspicious groups was not significant (p = 0.23). Upon re-review by a diagnostic radiologist, 25 (35%) scans deemed suspicious by radiation oncologists were confirmed to meet criteria, while the rest were re-classified as "atypical" for COVID-19. We conclude that radiotherapy simulation CT scans can be reviewed for signs of COVID-19 pneumonia by radiation oncologists. However, suspicious CT simulation was not associated with a higher incidence of COVID infection compared with non-suspicious CT simulation, and there was low concordance between radiation oncologist and diagnostic radiologist classification of scans.
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Affiliation(s)
- Abraham J. Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America,Corresponding author
| | - Andrew Plodkowski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Michelle Ginsberg
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Jacob Shin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Quincey LaPlant
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Annemarie Shepherd
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Narek Shaverdian
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Victor Ng
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Daphna Gelblum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Lior Braunstein
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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13
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Garg M, Karami V, Moazen J, Kwee T, Bhalla AS, Shahbazi-Gahrouei D, Shao YHJ. Radiation Exposure and Lifetime Attributable Risk of Cancer Incidence and Mortality from Low- and Standard-Dose CT Chest: Implications for COVID-19 Pneumonia Subjects. Diagnostics (Basel) 2022; 12:3043. [PMID: 36553050 PMCID: PMC9777015 DOI: 10.3390/diagnostics12123043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/07/2022] Open
Abstract
Since the novel coronavirus disease 2019 (COVID-19) outbreak, there has been an unprecedented increase in the acquisition of chest computed tomography (CT) scans. Nearly 616 million people have been infected by COVID-19 worldwide to date, of whom many were subjected to CT scanning. CT exposes the patients to hazardous ionizing radiation, which can damage the genetic material in the cells, leading to stochastic health effects in the form of heritable genetic mutations and increased cancer risk. These probabilistic, long-term carcinogenic effects of radiation can be seen over a lifetime and may sometimes take several decades to manifest. This review briefly describes what is known about the health effects of radiation, the lowest dose for which there exists compelling evidence about increased radiation-induced cancer risk and the evidence regarding this risk at typical CT doses. The lifetime attributable risk (LAR) of cancer from low- and standard-dose chest CT scans performed in COVID-19 subjects is also discussed along with the projected number of future cancers that could be related to chest CT scans performed during the COVID-19 pandemic. The LAR of cancer Incidence from chest CT has also been compared with those from other radiation sources, daily life risks and lifetime baseline risk.
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Affiliation(s)
- Mandeep Garg
- Department of Radiodiagnosis & Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Vahid Karami
- Clinical Research Development Unite, Ganjavian Hospital, Dezful University of Medical Sciences, Dezful 6461653476, Iran
| | - Javad Moazen
- Infectious and Tropical Diseases Research Center, School of Medicine, Dezful University of Medical Sciences, Dezful 6461653480, Iran
| | - Thomas Kwee
- Department of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Hanzeplein, 9700 Groningen, The Netherlands
| | - Ashu Seith Bhalla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Yu-Hsuan Joni Shao
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
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14
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Tawk S, Mansour W, Sleiman D, Gemayel S, Lozom E, El Mendelek K, Saliba N, Mourad C. Submillisievert CT chest for COVID-19 patients in a rural hospital with limited resources. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8894825 DOI: 10.1186/s43055-022-00737-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background This is a secondary analysis of prospectively acquired data approved by the hospital institutional board committee. We performed a retrospective chart review of 463 patients who underwent a CT Chest for suspected COVID-19 infection between April 1st, 2020, and March 31st, 2021. Patients were grouped based on the CT chest obtained protocol: ultra-low dose or full dose. The likelihood of suspicion of COVID-19 infection was classified on a Likert scale based on the probability of pulmonary involvement. For each group, the sensitivity and specificity of CT were compared to nasopharyngeal swab as standard of reference. The median dose length product and duration of apnea were compared between both groups using two-tailed Mann–Whitney U test. The aim of this study is to share our experience of reducing radiation dose in COVID-19 patients by using an ultra-low dose CT chest protocol on a 16 row multidetector CT scan in a hospital with limited resources. Results Two hundred sixty-nine patients underwent a full dose CT and 194 patients an ultra-low dose CT. In the former group, the median dose length product was 341.11 mGy*cm [Interquartile range (IQR), 239.1–443.2] and the median duration of apnea was 13.29 s [IQR, 10.85–15.73]. In the latter group, the median dose length product was 30.8 mGy*cm [IQR, 28.9–32.7] and median duration of apnea was 8.27 s [IQR, 7.69–8.85]. The sensitivity of the ultra-low dose CT was 91.2% and that of the full dose was 94%. Conclusion A 90% reduction in estimated dose and 38% reduction in apnea duration could be achieved using an ultra-low dose CT chest protocol on a 16-row MDCT without significant loss in the sensitivity of CT to detect COVID-related parenchymal involvement.
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15
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Bahrami-Motlagh H, Moharamzad Y, Izadi Amoli G, Abbasi S, Abrishami A, Khazaei M, Davarpanah AH, Sanei Taheri M. Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8727972 DOI: 10.1186/s43055-021-00689-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic.
Results 167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97; P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001). Conclusion Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.
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16
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Pushparaj TL, Irudaya Raj EF, Irudaya Rani EF. A detailed review of contrast-enhanced fluorescence magnetic resonance imaging techniques for earlier prediction and easy detection of COVID-19. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2144762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
| | - E. Fantin Irudaya Raj
- Department of Electrical and Electronics Engineering, Dr. Sivanthi Aditanar College of Engineering, India
| | - E. Francy Irudaya Rani
- Department of Electronics and Communication Engineering, Francis Xavier Engineering College, India
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17
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Kumar S, Mallik A. COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach. Neural Process Lett 2022; 55:1-24. [PMID: 36339644 PMCID: PMC9616430 DOI: 10.1007/s11063-022-11060-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2022] [Indexed: 10/31/2022]
Abstract
The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread of the disease. In this paper, we propose a novel Trained Output-based Transfer Learning (TOTL) approach for COVID-19 detection from chest X-rays. We start by preprocessing the Chest X-rays of the patients with techniques like denoising, contrasting, segmentation. These processed images are then fed to several pre-trained transfer learning models like InceptionV3, InceptionResNetV2, Xception, MobileNet, ResNet50, ResNet50V2, VGG16, and VGG19. We fine-tune these models on the processed chest X-rays. Then we further train the outputs of these models using a deep neural network architecture to achieve enhanced performance and aggregate the capabilities of each of them. The proposed model has been tested on four recent COVID-19 chest X-rays datasets by computing several popular evaluation metrics. The performance of our model has also been compared with various deep transfer learning models and several contemporary COVID-19 detection methods. The obtained results demonstrate the efficiency and efficacy of our proposed model.
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Affiliation(s)
- Sanjay Kumar
- Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042 India
| | - Abhishek Mallik
- Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042 India
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18
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Čiva LM, Beganović A, Busuladžić M, Jusufbegović M, Awad-Dedić T, Vegar-Zubović S. Dose Descriptors and Assessment of Risk of Exposure-Induced Death in Patients Undergoing COVID-19 Related Chest Computed Tomography. Diagnostics (Basel) 2022; 12:2012. [PMID: 36010362 PMCID: PMC9407529 DOI: 10.3390/diagnostics12082012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/14/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
For more than two years, coronavirus disease 19 (COVID-19) has represented a threat to global health and lifestyles. Computed tomography (CT) imaging provides useful information in patients with COVID-19 pneumonia. However, this diagnostic modality is based on exposure to ionizing radiation, which is associated with an increased risk of radiation-induced cancer. In this study, we evaluated the common dose descriptors, CTDIvol and DLP, for 1180 adult patients. This data was used to estimate the effective dose, and risk of exposure-induced death (REID). Awareness of the extensive use of CT as a diagnostic tool in the management of COVID-19 during the pandemic is vital for the evaluation of radiation exposure parameters, dose reduction methods development and radiation protection.
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Affiliation(s)
- Lejla M. Čiva
- Sarajevo Medical School, University Sarajevo School of Science and Technology, 71210 Ilidža, Bosnia and Herzegovina
| | - Adnan Beganović
- Radiation Protection and Medical Physics Department, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
- Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mustafa Busuladžić
- Faculty of Medicine, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Merim Jusufbegović
- Radiology Clinic, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
| | - Ta’a Awad-Dedić
- Healthcare Center of Sarajevo Canton, 71000 Sarajevo, Bosnia and Herzegovina
| | - Sandra Vegar-Zubović
- Radiology Clinic, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
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19
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Zarei F, Jalli R, Chatterjee S, Ravanfar Haghighi R, Iranpour P, Vardhan Chatterjee V, Emadi S. Evaluation of Ultra-Low-Dose Chest Computed Tomography Images in Detecting Lung Lesions Related to COVID-19: A Prospective Study. IRANIAN JOURNAL OF MEDICAL SCIENCES 2022; 47:338-349. [PMID: 35919083 PMCID: PMC9339117 DOI: 10.30476/ijms.2021.90665.2165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/23/2021] [Accepted: 09/11/2021] [Indexed: 11/04/2022]
Abstract
Background The present study aimed to evaluate the effectiveness of ultra-low-dose (ULD) chest computed tomography (CT) in comparison with the routine dose (RD) CT images in detecting lung lesions related to COVID-19. Methods A prospective study was conducted during April-September 2020 at Shahid Faghihi Hospital affiliated with Shiraz University of Medical Sciences, Shiraz, Iran. In total, 273 volunteers with suspected COVID-19 participated in the study and successively underwent RD-CT and ULD-CT chest scans. Two expert radiologists qualitatively evaluated the images. Dose assessment was performed by determining volume CT dose index, dose length product, and size-specific dose estimate. Data analysis was performed using a ranking test and kappa coefficient (κ). P<0.05 was considered statistically significant. Results Lung lesions could be detected with both RD-CT and ULD-CT images in patients with suspected or confirmed COVID-19 (κ=1.0, P=0.016). The estimated effective dose for the RD-CT protocol was 22-fold higher than in the ULD-CT protocol. In the case of the ULD-CT protocol, sensitivity, specificity, accuracy, and positive predictive value for the detection of consolidation were 60%, 83%, 80%, and 20%, respectively. Comparably, in the case of RD-CT, these percentages for the detection of ground-glass opacity (GGO) were 62%, 66%, 66%, and 18%, respectively. Assuming the result of real-time polymerase chain reaction as true-positive, analysis of the receiver-operating characteristic curve for GGO detected using the ULD-CT protocol showed a maximum area under the curve of 0.78. Conclusion ULD-CT, with 94% dose reduction, can be an alternative to RD-CT to detect lung lesions for COVID-19 diagnosis and follow-up.An earlier preliminary report of a similar work with a lower sample size was submitted to the arXive as a preprint. The preprint is cited as: https://arxiv.org/abs/2005.03347.
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Affiliation(s)
- Fariba Zarei
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Jalli
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | | | - Pooya Iranpour
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Vani Vardhan Chatterjee
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Sedigheh Emadi
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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20
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Low-Dose High-Resolution Photon-Counting CT of the Lung: Radiation Dose and Image Quality in the Clinical Routine. Diagnostics (Basel) 2022; 12:diagnostics12061441. [PMID: 35741251 PMCID: PMC9221815 DOI: 10.3390/diagnostics12061441] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/04/2022] [Accepted: 06/07/2022] [Indexed: 01/09/2023] Open
Abstract
This study aims to investigate the qualitative and quantitative image quality of low-dose high-resolution (LD-HR) lung CT scans acquired with the first clinical approved photon counting CT (PCCT) scanner. Furthermore, the radiation dose used by the PCCT is compared to a conventional CT scanner with an energy-integrating detector system (EID-CT). Twenty-nine patients who underwent a LD-HR chest CT scan with dual-source PCCT and had previously undergone a LD-HR chest CT with a standard EID-CT scanner were retrospectively included in this study. Images of the whole lung as well as enlarged image sections displaying a specific finding (lesion) were evaluated in terms of overall image quality, image sharpness and image noise by three senior radiologists using a 5-point Likert scale. The PCCT images were reconstructed with and without a quantum iterative reconstruction algorithm (PCCT QIR+/−). Noise and signal-to-noise (SNR) were measured and the effective radiation dose was calculated. Overall, image quality and image sharpness were rated best in PCCT (QIR+) images. A significant difference was seen particularly in image sections of PCCT (QIR+) images compared to EID-CT images (p < 0.005). Image noise of PCCT (QIR+) images was significantly lower compared to EID-CT images in image sections (p = 0.005). In contrast, noise was lowest on EID-CT images (p < 0.001). The PCCT used significantly less radiation dose compared to the EID-CT (p < 0.001). In conclusion, LD-HR PCCT scans of the lung provide better image quality while using significantly less radiation dose compared to EID-CT scans.
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21
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Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 1.0 Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans. Diagnostics (Basel) 2022; 12:1283. [PMID: 35626438 PMCID: PMC9141749 DOI: 10.3390/diagnostics12051283] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Armin Mehmedović
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95661, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Klaudija Viskovic
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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22
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Martini K, Larici AR, Revel MP, Ghaye B, Sverzellati N, Parkar AP, Snoeckx A, Screaton N, Biederer J, Prosch H, Silva M, Brady A, Gleeson F, Frauenfelder T. COVID-19 pneumonia imaging follow-up: when and how? A proposition from ESTI and ESR. Eur Radiol 2022; 32:2639-2649. [PMID: 34713328 PMCID: PMC8553396 DOI: 10.1007/s00330-021-08317-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/20/2021] [Accepted: 09/04/2021] [Indexed: 12/26/2022]
Abstract
This document from the European Society of Thoracic Imaging (ESTI) and the European Society of Radiology (ESR) discusses the role of imaging in the long-term follow-up of COVID-19 patients, to define which patients may benefit from imaging, and what imaging modalities and protocols should be used. Insights into imaging features encountered on computed tomography (CT) scans and potential pitfalls are discussed and possible areas for future review and research are also included. KEY POINTS: • Post-COVID-19 pneumonia changes are mainly consistent with prior organizing pneumonia and are likely to disappear within 12 months of recovery from the acute infection in the majority of patients. • At present, with the longest series of follow-up examinations reported not exceeding 12 months, the development of persistent or progressive fibrosis in at least some individuals cannot yet be excluded. • Residual ground glass opacification may be associated with persisting bronchial dilatation and distortion, and might be termed "fibrotic-like changes" probably consistent with prior organizing pneumonia.
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Affiliation(s)
- K Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - A R Larici
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - M P Revel
- Department of Radiology, Cochin Hospital, Université de Paris, Paris, France
| | - B Ghaye
- Department of Radiology, Cliniques Universitaires Saint Luc, Catholic University of Louvain, Brussels, Belgium
| | - N Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - A P Parkar
- Department of Radiology, Haraldsplass Deaconess Hospital and Department of Clinical Medicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - A Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
| | - N Screaton
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - J Biederer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Member of the German Lung Research Center (DZL), Translational Lung Research Center Heidelberg (TLRC), Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
- Faculty of Medicine, University of Latvia, Raina bulvaris 19, Riga, 1586, Latvia
- Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, 24098, Kiel, Germany
| | - H Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - M Silva
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - A Brady
- Department of Radiology, Mercy University Hospital, Cork, and University College Cork, Cork, Ireland
| | - F Gleeson
- Department of Oncology, University of Oxford, Oxford, UK
| | - T Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
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23
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Afshar P, Rafiee MJ, Naderkhani F, Heidarian S, Enshaei N, Oikonomou A, Babaki Fard F, Anconina R, Farahani K, Plataniotis KN, Mohammadi A. Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network. Sci Rep 2022; 12:4827. [PMID: 35318368 PMCID: PMC8940967 DOI: 10.1038/s41598-022-08796-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/01/2022] [Indexed: 01/01/2023] Open
Abstract
Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
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Affiliation(s)
- Parnian Afshar
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University Health Center-Research Institute, Montreal, QC, Canada
| | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Nastaran Enshaei
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | | | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), Rockville, MD, USA
| | | | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada.
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24
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Jin KN, Do KH, Nam BD, Hwang SH, Choi M, Yong HS. [Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:265-283. [PMID: 36237918 PMCID: PMC9514447 DOI: 10.3348/jksr.2021.0117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/10/2021] [Accepted: 09/17/2021] [Indexed: 06/16/2023]
Abstract
To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.
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25
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Bourdoncle S, Eche T, McGale J, Yiu K, Partouche E, Yeh R, Ammari S, Rousseau H, Dercle L, Mokrane FZ. Investigating of the role of CT scan for cancer patients during the first wave of COVID-19 pandemic. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022. [PMID: 37520011 PMCID: PMC8970534 DOI: 10.1016/j.redii.2022.100004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Introduction Amidst this current COVID-19 pandemic, we undertook this systematic review to determine the role of medical imaging, with a special emphasis on computed tomography (CT), on guiding the care and management of oncologic patients. Material and Methods Study selection focused on articles from 01/02/2020 to 04/23/2020. After removal of irrelevant articles, all systematic or non-systematic reviews, comments, correspondence, editorials, guidelines and meta-analysis and case reports with less than 5 patients were also excluded. Full-text articles of eligible publications were reviewed to select all imaging-based publications, and the existence or not of an oncologic population was reported for each publication. Two independent reviewers collected the following information: ( 1) General publication data; (2) Study design characteristics; (3) Demographic, clinical and pathological variables with percentage of cancer patients if available; (4) Imaging performances. The sensitivity and specificity of chest CT (C-CT) were pooled separately using a random-effects model. The positive predictive value (PPV) and negative predictive value (NPV) of C-CT as a test was estimated for a wide range of disease prevalence rates. Results A total of 106 publications were fully reviewed. Among them, 96 were identified to have extractable data for a two-by-two contingency table for CT performance. At the end, 53 studies (including 6 that used two different populations) were included in diagnosis accuracy analysis (N = 59). We identified 53 studies totaling 11,352 patients for whom the sensitivity (95CI) was 0.886 (0.880; 0.894), while specificity remained low: in 93% of cases (55/59), specificity was ≤ 0.5. Among all the 106 reviewed studies, only 7 studies included oncologic patients and were included in the final analysis for C-CT performances. The percentage of patients with cancer in these studies was 0.3% (34/11352 patients), lower than the global prevalence of cancer. Among all these studies, only 1 (0.9%, 1/106) reported performance specifically in a cohort of cancer patients, but it however only reported true positives. Discussion There is a concerning lack of COVID-19 studies involving oncologic patients, showing there is a real need for further investigation and evaluation of the performance of the different medical imaging modalities in this specific patient population.
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Chalkia M, Arkoudis NA, Maragkoudakis E, Rallis S, Tremi I, Georgakilas AG, Kouloulias V, Efstathopoulos E, Platoni K. The Role of Ionizing Radiation for Diagnosis and Treatment against COVID-19: Evidence and Considerations. Cells 2022; 11:467. [PMID: 35159277 PMCID: PMC8834503 DOI: 10.3390/cells11030467] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic continues to spread worldwide with over 260 million people infected and more than 5 million deaths, numbers that are escalating on a daily basis. Frontline health workers and scientists diligently fight to alleviate life-threatening symptoms and control the spread of the disease. There is an urgent need for better triage of patients, especially in third world countries, in order to decrease the pressure induced on healthcare facilities. In the struggle to treat life-threatening COVID-19 pneumonia, scientists have debated the clinical use of ionizing radiation (IR). The historical literature dating back to the 1940s contains many reports of successful treatment of pneumonia with IR. In this work, we critically review the literature for the use of IR for both diagnostic and treatment purposes. We identify details including the computed tomography (CT) scanning considerations, the radiobiological basis of IR anti-inflammatory effects, the supportive evidence for low dose radiation therapy (LDRT), and the risks of radiation-induced cancer and cardiac disease associated with LDRT. In this paper, we address concerns regarding the effective management of COVID-19 patients and potential avenues that could provide empirical evidence for the fight against the disease.
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Affiliation(s)
- Marina Chalkia
- 2nd Department of Radiology, Medical Physics Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (S.R.); (E.E.); (K.P.)
| | - Nikolaos-Achilleas Arkoudis
- 2nd Department of Radiology, Diagnostic Radiology Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece;
| | - Emmanouil Maragkoudakis
- 2nd Department of Radiology, Radiation Oncology Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.M.); (V.K.)
| | - Stamatis Rallis
- 2nd Department of Radiology, Medical Physics Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (S.R.); (E.E.); (K.P.)
| | - Ioanna Tremi
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), 15780 Athens, Greece; (I.T.); (A.G.G.)
| | - Alexandros G. Georgakilas
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), 15780 Athens, Greece; (I.T.); (A.G.G.)
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiation Oncology Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.M.); (V.K.)
| | - Efstathios Efstathopoulos
- 2nd Department of Radiology, Medical Physics Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (S.R.); (E.E.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Medical Physics Unit, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece; (S.R.); (E.E.); (K.P.)
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Thieß HM, Bressem KK, Adams L, Böning G, Vahldiek JL, Niehues SM. Do submillisievert-chest CT protocols impact diagnostic quality in suspected COVID-19 patients? Acta Radiol Open 2022; 11:20584601211073864. [PMID: 35096416 PMCID: PMC8796096 DOI: 10.1177/20584601211073864] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background During the ongoing global SARS-CoV-2 pandemic, there is a high demand for quick and reliable methods for early identification of infected patients. Due to its widespread availability, chest-CT is commonly used to detect early pulmonary manifestations and for follow-ups. Purpose This study aims to analyze image quality and reproducibility of readings of scans using low-dose chest CT protocols in patients suspected of SARS-CoV-2 infection. Materials and Methods Two radiologists retrospectively analyzed 100 low-dose chest CT scans of patients suspected of SARS-CoV-2 infection using two protocols on devices from two vendors regarding image quality based on a Likert scale. After 3 weeks, quality ratings were repeated to allow for analysis of intra-reader in addition to the inter-reader agreement. Furthermore, radiation dose and presence as well as distribution of radiological features were noted. Results The exams’ effective radiation doses were in median in the submillisievert range (median of 0.53 mSv, IQR: 0.35 mSv). While most scans were rated as being of optimal quality, 38% of scans were scored as suboptimal, yet only one scan was non-diagnostic. Inter-reader and intra-reader reliability showed almost perfect agreement with Cohen’s kappa of 0.82 and 0.87. Conclusion Overall, in this study, we present two protocols for submillisievert low-dose chest CT demonstrating appropriate or better image quality with almost perfect inter-reader and intra-reader agreement in patients suspected of SARS-CoV-2 infection.
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Affiliation(s)
- Hans-Martin Thieß
- Department of Radiology, Charité Universitätsmedizin Berlin Campus Benjamin Franklin, Berlin, Germany
| | - Keno K Bressem
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Adams
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Böning
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan M Niehues
- Klinik für Radiologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Qureshi SA, Rehman AU, Mir AA, Rafique M, Muhammad W. Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography. Front Physiol 2022; 12:737233. [PMID: 35095544 PMCID: PMC8795832 DOI: 10.3389/fphys.2021.737233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022] Open
Abstract
The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.
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Affiliation(s)
- Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
| | - Aziz Ul Rehman
- Agri & Biophotonics Division, National Institute of Lasers and Optronics College, PIEAS, Islamabad, Pakistan
| | - Adil Aslam Mir
- Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara, Turkey
- Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, The University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan
| | - Muhammad Rafique
- Department of Physics, King Abdullah Campus Chatter Kalas, The University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan
| | - Wazir Muhammad
- Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, United States
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Aiello M, Baldi D, Esposito G, Valentino M, Randon M, Salvatore M, Cavaliere C. Evaluation of AI-Based Segmentation Tools for COVID-19 Lung Lesions on Conventional and Ultra-low Dose CT Scans. Dose Response 2022; 20:15593258221082896. [PMID: 35422680 PMCID: PMC9002358 DOI: 10.1177/15593258221082896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/04/2022] [Indexed: 11/16/2022] Open
Abstract
A reliable diagnosis and accurate monitoring are pivotal steps for treatment and prevention of COVID-19. Chest computed tomography (CT) has been considered a crucial diagnostic imaging technique for the injury assessment of the viral pneumonia. Furthermore, the automatization of the segmentation methods for lung alterations helps to speed up the diagnosis and lighten radiologists' workload. Considering the assiduous pathology monitoring, ultra-low dose (ULD) chest CT protocols have been implemented to drastically reduce the radiation burden. Unfortunately, the available AI technologies have not been trained on ULD-CT data and validated and their applicability deserves careful evaluation. Therefore, this work aims to compare the results of available AI tools (BCUnet, CORADS AI, NVIDIA CLARA Train SDK and CT Pneumonia Analysis) on a dataset of 73 CT examinations acquired both with conventional dose (CD) and ULD protocols. COVID-19 volume percentage, resulting from each tool, was statistically compared. This study demonstrated high comparability of the results on CD-CT and ULD-CT data among the four AI tools, with high correlation between the results obtained on both protocols (R > .68, P < .001, for all AI tools).
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Affiliation(s)
| | | | | | - Marika Valentino
- Istituto di Scienze Applicate e
Sistemi Intelligenti “Eduardo Caianiello” (ISASI-CNR), Pozzuoli, Italy
- Università Degli Studi di Napoli
Federico II, Dip. di Ingegneria Elettrica e Delle Tecnologie
Dell'Informazione, Italy
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Samir A, El-Husseiny RM, Sweed RA, El-Maaboud NAEMA, Masoud M. Ultra-low-dose chest CT protocol during the second wave of COVID-19 pandemic: a double-observer prospective study on 250 patients to evaluate its detection accuracy. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [PMCID: PMC8150152 DOI: 10.1186/s43055-021-00512-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background While the second wave of COVID-19 pandemic almost reached its climax, unfortunately, new viral strains are rapidly spreading, and numbers of infected young adults are rising. Consequently, chest high-resolution computed tomography (HRCT) demands are increasing, regarding patients’ screening, initial evaluation and follow up. This study aims to evaluate the detection accuracy of ultra-low-dose chest CT in comparison with the routine low-dose chest CT to reduce the irradiation exposure hazards. Results This study was prospectively conducted on 250 patients during the period from 15th December 2020 to 10th February 2021. All of the included patients were clinically suspected of COVID-19 infection. All patients were subjected to routine low-dose (45 mAs) and ultra-low-dose (22 mAs) chest CT examinations. Finally, all patients had confirmatory PCR swab tests and other dedicated laboratory tests. They included 149 males and 101 females (59.6%:40.4%). Their age ranged from 16 to 84 years (mean age 50 ± 34 SD). Patients were divided according to body weight; 104 patients were less than 80 kg, and 146 patients were more than 80 kg. HRCT findings were examined by two expert consultant radiologists independently, and data analysis was performed by other two expert specialist and consultant radiologists. The inter-observer agreement (IOA) was excellent (96–100%). The ultra-low-dose chest CT reached 93.53–96.84% sensitivity and 90.38–93.84% accuracy. The signal-to-noise ratio (SNR) is 12.8:16.1; CTDIvol (mGy) = 1.1 ± 0.3, DLP (mGy cm) = 42.2 ± 7.9, mean effective dose (mSv/mGy cm) = 0.59 and absolute cancer risk = 0.02 × 10-4. Conclusion Ultra-low-dose HRCT can be reliably used during the second wave of COVID-19 pandemic to reduce the irradiation exposure hazards.
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Bhardwaj P, Kaur A. A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021; 31:1775-1791. [PMID: 34518739 PMCID: PMC8426690 DOI: 10.1002/ima.22627] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/06/2021] [Accepted: 06/27/2021] [Indexed: 05/03/2023]
Abstract
With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19, 2022 pneumonia, and 5863 normal chest X-ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre-processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.
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Affiliation(s)
| | - Amanpreet Kaur
- Electronics and CommunicationThapar UniversityPatialaIndia
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Hosseini Nasab SMB, Deevband MR, Rahimi R, Nasiri S, Ahangaran MR, Morshedi M. OPTIMIZATION OF LUNG CT PROTOCOL FOR THE DIAGNOSTIC EVALUATION OF COVID-19 LUNG DISEASE. RADIATION PROTECTION DOSIMETRY 2021; 196:120-127. [PMID: 34557925 PMCID: PMC8500036 DOI: 10.1093/rpd/ncab140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
This study intends to evaluate the different lung CT scan protocols used for the diagnostic evaluation of COVID-19-induced lung disease in Iranian imaging centers in terms of radiation dose and image quality. After data collecting, subjective image quality, radiation dose and objective image quality such as noise, SNR and CNR were assessed. Statistically significant differences in effective dose and image quality were evident among different lung CT protocols. Lowest and highest effective dose was1.31 ± 0.53 mSv related to a protocol with activated AEC (reference mAs = 20) and 6.15 ± 0.57 mSv related to a protocol with Fixed mAs (mAs = 100), respectively. A protocol with enabled tube current modulation with 70 mAs as a reference mAs, and protocol with 20 mAs and enabled AEC had the best and lowest image quality, respectively. To optimize the scan parameters, AEC must be used, and a range of tube currents (between 20 and 50 mAs) can produce acceptable images in terms of diagnostic quality and radiation dose for the diagnosis of COVID-19-induced lung disease.
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Affiliation(s)
| | - Mohammad Reza Deevband
- Department of Medical Physics and Biomedical engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences and Health Services, Tehran, Iran
| | - Roghaye Rahimi
- Radiology Department, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeed Nasiri
- Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Mina Morshedi
- Radiology Department, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Lee JH, Hong H, Kim H, Lee CH, Goo JM, Yoon SH. CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict. TAEHAN YONGSANG UIHAKHOE CHI 2021; 82:1505-1523. [PMID: 36238884 PMCID: PMC9431975 DOI: 10.3348/jksr.2021.0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 05/31/2023]
Abstract
Purpose Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.
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Qi S, Xu C, Li C, Tian B, Xia S, Ren J, Yang L, Wang H, Yu H. DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106406. [PMID: 34536634 PMCID: PMC8426140 DOI: 10.1016/j.cmpb.2021.106406] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 09/02/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial. METHODS We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models. RESULTS DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation. CONCLUSIONS DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.
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Affiliation(s)
- Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Caiwen Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Bin Tian
- Department of Radiology, The Second People's Hospital of Guiyang, Guiyang, China
| | - Shuyue Xia
- Department of Respiratory Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China
| | - Jigang Ren
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Liming Yang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Hanlin Wang
- Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China.
| | - Hui Yu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
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Gashi A, Kubik-Huch RA, Chatzaraki V, Potempa A, Rauch F, Grbic S, Wiggli B, Friedl A, Niemann T. Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software tool. Medicine (Baltimore) 2021; 100:e27478. [PMID: 34731126 PMCID: PMC8519217 DOI: 10.1097/md.0000000000027478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/19/2021] [Accepted: 09/17/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT The COVID-19 pandemic has challenged institutions' diagnostic processes worldwide. The aim of this study was to assess the feasibility of an artificial intelligence (AI)-based software tool that automatically evaluates chest computed tomography for findings of suspected COVID-19.Two groups were retrospectively evaluated for COVID-19-associated ground glass opacities of the lungs (group A: real-time polymerase chain reaction positive COVID patients, n = 108; group B: asymptomatic pre-operative group, n = 88). The performance of an AI-based software assessment tool for detection of COVID-associated abnormalities was compared with human evaluation based on COVID-19 reporting and data system (CO-RADS) scores performed by 3 readers.All evaluated variables of the AI-based assessment showed significant differences between the 2 groups (P < .01). The inter-reader reliability of CO-RADS scoring was 0.87. The CO-RADS scores were substantially higher in group A (mean 4.28) than group B (mean 1.50). The difference between CO-RADS scoring and AI assessment was statistically significant for all variables but showed good correlation with the clinical context of the CO-RADS score. AI allowed to predict COVID positive cases with an accuracy of 0.94.The evaluated AI-based algorithm detects COVID-19-associated findings with high sensitivity and may support radiologic workflows during the pandemic.
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Affiliation(s)
- Andi Gashi
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, ETH Zurich, 101 Rämistrasse, Zurich, Switzerland
| | - Rahel A. Kubik-Huch
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Vasiliki Chatzaraki
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Anna Potempa
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Franziska Rauch
- Siemens Healthcare GmbH, 3 Siemensstrasse, Forchheim, Germany
| | - Sasa Grbic
- Siemens Healthcare GmbH, 3 Siemensstrasse, Forchheim, Germany
| | - Benedikt Wiggli
- Department of Infectious Diseases, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Andrée Friedl
- Department of Infectious Diseases, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Tilo Niemann
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
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Gross A, Albrecht T. One year of COVID-19 pandemic: what we Radiologists have learned about imaging. ROFO-FORTSCHR RONTG 2021; 194:141-151. [PMID: 34649291 DOI: 10.1055/a-1522-3155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Since its outbreak in December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has infected more than 151 million people worldwide. More than 3.1 million have died from Coronavirus Disease 2019 (COVID-19), the illness caused by SARS-CoV-2. The virus affects mainly the upper respiratory tract and the lungs causing pneumonias of varying severity. Moreover, via direct and indirect pathogenetic mechanisms, SARS-CoV-2 may lead to a variety of extrapulmonary as well as vascular manifestations. METHODS Based on a systematic literature search via PubMed, original research articles, meta-analyses, reviews, and case reports representing the current scientific knowledge regarding diagnostic imaging of COVID-19 were selected. Focusing on the imaging appearance of pulmonary and extrapulmonary manifestations as well as indications for imaging, these data were summarized in the present review article and correlated with basic pathophysiologic mechanisms. RESULTS AND CONCLUSION Typical signs of COVID-19 pneumonia are multifocal, mostly bilateral, rounded, polycyclic or geographic ground-glass opacities and/or consolidations with mainly peripheral distribution. In severe cases, peribronchovascular lung zones are affected as well. Other typical signs are the "crazy paving" pattern and the halo and reversed halo (the latter two being less common). Venous thromboembolism (and pulmonary embolism in particular) is the most frequent vascular complication of COVID-19. However, arterial thromboembolic events like ischemic strokes, myocardial infarctions, and systemic arterial emboli also occur at higher rates. The most frequent extrapulmonary organ manifestations of COVID-19 affect the central nervous system, the heart, the hepatobiliary system, and the gastrointestinal tract. Usually, they can be visualized in imaging studies as well. The most important imaging modality for COVID-19 is chest CT. Its main purpose is not to make the primary diagnosis, but to differentiate COVID-19 from other (pulmonary) pathologies, to estimate disease severity, and to detect concomitant diseases and complications. KEY POINTS · Typical signs of COVID-19 pneumonia are multifocal, mostly peripheral ground-glass opacities/consolidations.. · Imaging facilitates differential diagnosis, estimation of disease severity, and detection of complications.. · Venous thromboembolism (especially pulmonary embolism) is the predominant vascular complication of COVID-19.. · Arterial thromboembolism (e. g., ischemic strokes, myocardial infarctions) occurs more frequently as well.. · The most common extrapulmonary manifestations affect the brain, heart, hepatobiliary system, and gastrointestinal system.. CITATION FORMAT · Gross A, Albrecht T. One year of COVID-19 pandemic: what we Radiologists have learned about imaging. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1522-3155.
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Affiliation(s)
- Alexander Gross
- Radiology and Interventional Therapy, Vivantes-Klinikum Neukölln, Berlin, Germany
| | - Thomas Albrecht
- Radiology and Interventional Therapy, Vivantes-Klinikum Neukölln, Berlin, Germany
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Bai L, Zhou J, Shen C, Cai S, Guo Y, Huang X, Jia G, Niu G. Assessment of radiation doses and image quality of multiple low-dose CT exams in COVID-19 clinical management. CHINESE JOURNAL OF ACADEMIC RADIOLOGY 2021; 4:257-261. [PMID: 34642650 PMCID: PMC8498979 DOI: 10.1007/s42058-021-00083-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 08/22/2021] [Accepted: 09/09/2021] [Indexed: 06/13/2023]
Abstract
PURPOSE The Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 from an outbreak of unexplained pneumonia in Wuhan (Hubei, China) that subsequently spread rapidly around the world. Because of the public health emergency, chest CT has been widely used for sensitive detection and diagnosis, monitoring the changes of lesions and also for treatment evaluation. The purpose of this study was to investigate radiation dose and image quality of chest CT scans received by COVID-19 patients and to evaluate the oncogenic risk of multiple chest CT examinations. METHODS A retrospective review of 33 patients with RT-PCR confirmed COVID-19 infection was performed from January 31, 2020 to February 19, 2020. The date of each CT exam and respective radiation dose for each exam was recorded for all patients. Multiple pulmonary CT scans were obtained during diagnosis and treatment procedure. Scan frequency, total scan times, radiation dose, and image quality were determined. RESULTS Thirty-three patients (15 males and 18 females, age 21-82 years) with confirmed COVID-19 pneumonia underwent a total of 143 chest CT scans. The number of CT scans per patient was 4 ± 1, with a range of 2-6. The time interval between two consecutive chest CT scans was 3 ± 1 days. The average effective dose from a single chest CT scan was 1.21 ± 0.10 mSv, with a range of 1.02-1.44 mSv. The average cumulative effective dose per patient was 5.25 ± 1.52 mSv, with a range of 2.24-7.48 mSv. The maximum cumulative effective dose was 7.48 mSv for six CT examinations during COVID-19 treatment. Based on subjective image quality analysis, the visual scoring of CT findings was 11.23 ± 1.35 points out of 15 points. CONCLUSIONS The frequency, total number and image quality of chest CT scans should be reviewed carefully to guarantee minimally required CT scans during the COVID-19 management.
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Affiliation(s)
- Lu Bai
- Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi China
| | - Jie Zhou
- Department of Radiology, Xi’an Chest Hospital, Xi’an, Shaanxi China
| | - Cong Shen
- Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi China
| | - Shubo Cai
- Department of Radiology, Xi’an Chest Hospital, Xi’an, Shaanxi China
| | - Youmin Guo
- Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi China
| | - Xunan Huang
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi’an, 710071 Shaanxi China
| | - Guang Jia
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi’an, 710071 Shaanxi China
| | - Gang Niu
- Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi China
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Garg M, Prabhakar N, Bhalla AS. Cancer risk of CT scan in COVID-19: Resolving the dilemma. Indian J Med Res 2021; 153:568-571. [PMID: 34596597 PMCID: PMC8555607 DOI: 10.4103/ijmr.ijmr_1476_21] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Mandeep Garg
- Department of Radiodiagnosis & Imaging, Postgraduate Institute of Medical Education & Research, Chandigarh 160 012, India
| | - Nidhi Prabhakar
- Department of Radiodiagnosis & Imaging, Postgraduate Institute of Medical Education & Research, Chandigarh 160 012, India
| | - Ashu Seith Bhalla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi 110 029, India
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Won T, Lee AK, Choi HD, Lee C. Radiation dose from computed tomography scans for Korean pediatric and adult patients. JOURNAL OF RADIATION PROTECTION AND RESEARCH 2021; 46:98-105. [PMID: 38894707 PMCID: PMC11185358 DOI: 10.14407/jrpr.2021.00010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/03/2021] [Indexed: 06/21/2024]
Abstract
Background In recent events of the Coronavirus Disease 2019 (COVID-19) pandemic, CT scans are being globally used as a complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. It will be important to be aware of major organ dose levels, which are more relevant quantity to derive potential long-term adverse effect, for Korean pediatric and adult patients undergoing CT for COVID-19. Materials and Methods We calculated organ dose conversion coefficients for Korean pediatric and adult CT patients directly from Korean pediatric and adult computational phantoms combined with Monte Carlo radiation transport techniques. We then estimated major organ doses delivered to the Korean child and adult patients undergoing CT for COVID-19 combining the dose conversion coefficients and the international survey data. We also compared our Korean dose conversion coefficients with those from Caucasian reference pediatric and adult phantoms. Results and discussion Based on the dose conversion coefficients we established in this study and the international survey data of COVID-19-related CT scans, we found that Korean 7-year-old child and adult males may receive about 4 - 32 mGy and 3 - 21 mGy of lung dose, respectively. We learned that the lung dose conversion coefficient for the Korean child phantom was up to 1.5-fold greater than that for the Korean adult phantom. We also found no substantial difference in dose conversion coefficients between Korean and Caucasian phantoms. Conclusion We estimated radiation dose delivered to the Korean child and adult phantoms undergoing COVID-19-related CT examinations. The dose conversion coefficients derived for different CT scan types can be also used universally for other dosimetry studies concerning Korean CT scans. We also confirmed that the Caucasian-based CT organ dose calculation tools may be used for the Korean population with reasonable accuracy.
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Affiliation(s)
- Tristan Won
- Winston Churchill High School, Potomac, MD 20854
| | - Ae-Kyoung Lee
- Electronics and Telecommunications Research Institute, Daejeon, South Korea
| | - Hyung-do Choi
- Electronics and Telecommunications Research Institute, Daejeon, South Korea
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850
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Wang X, Jiang L, Li L, Xu M, Deng X, Dai L, Xu X, Li T, Guo Y, Wang Z, Dragotti PL. Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2463-2476. [PMID: 33983881 PMCID: PMC8544955 DOI: 10.1109/tmi.2021.3079709] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/30/2021] [Accepted: 05/09/2021] [Indexed: 05/13/2023]
Abstract
Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.
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Affiliation(s)
- Xiaofei Wang
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Lai Jiang
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Liu Li
- Department of ComputingImperial College LondonLondonSW7 2AZU.K.
| | - Mai Xu
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Xin Deng
- School of Cyber Science and TechnologyBeihang UniversityBeijing100191China
| | - Lisong Dai
- Liyuan HospitalHuazhong University of Science and TechnologyWuhan430077China
| | - Xiangyang Xu
- Liyuan HospitalHuazhong University of Science and TechnologyWuhan430077China
| | - Tianyi Li
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Yichen Guo
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Zulin Wang
- School of Electronic and Information EngineeringBeihang UniversityBeijing100191China
| | - Pier Luigi Dragotti
- Department of Electrical and Electronic EngineeringImperial College LondonLondonSW7 2AZU.K.
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Karakaş HM, Yıldırım G, Çiçek ED. The reliability of low-dose chest CT for the initial imaging of COVID-19: comparison of structured findings, categorical diagnoses and dose levels. Diagn Interv Radiol 2021; 27:607-614. [PMID: 34318757 PMCID: PMC8480955 DOI: 10.5152/dir.2021.20802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE The widespread use of computed tomography (CT) in COVID-19 may cause adverse biological effects. Many recommend to minimize radiation dose while maintaining diagnostic quality. This study was designed to evaluate the difference between findings of COVID-19 pneumonia on standard and low-dose protocols to provide data on the utility of the latter during initial imaging of COVID-19. METHODS Patients suspected of having COVID-19 were scanned with a 128-slices scanner using two consecutive protocols in the same session (standard-dose scan: 120 kV and 300 mA; low-dose scan: 80 kV and 40 mA). Dose data acquisition and analysis was performed using an automated software. High and low-dose examinations were anonymized, shuffled and read by two radiologist with consensus according to a highly structured reporting format that was primarily based on the consensus statement of the RSNA. Accordingly, 8 typical, 2 indeterminate, and 7 atypical findings were investigated. Cases were then assigned to one of the categories: (i) Cov19Typ, typical COVID-19; (ii) Cov19Ind, indeterminate COVID-19; (iii) Cov19Aty, atypical COVID-19; (iv) Cov19Neg, not COVID-19. McNemar test was used to analyze the number of disagreements between standard and low-dose scans regarding paired proportions of structured findings. Inter- test reliability was tested using kappa coefficient. RESULTS The study included 740 patients with a mean age of 44.05±16.59 years. The median (minimum-maximum) dose level for standard protocol was 189.98 mGy•cm (98.20-493.54 mGy•cm) and for low-dose protocol was 15.59 mGy•cm (11.59-32.37 mGy•cm) differing by -80 and -254 mGy•cm from pan-European diagnostic reference levels. Only two findings for typical, one finding for indeterminate, and three findings for atypical categories were statistically similar (p > 0.05). The difference in other categories resulted in significantly different final diagnosis for COVID-19 (p < 0.001). Overall, 626 patients received matching diagnoses with the two protocols. According to intertest reliability analysis, kappa value was found to be 0.669 (p < 0.001) to indicate substantial match. CT with standard-dose had a sensitivity of 94% and a specificity of 72%, while CT with low-dose had a sensitivity of 90% and a specificity of 81%. CONCLUSION Low kV and mA scans, as used in this study according to scanner manufacturer's global recommendations, may significantly lower exposure levels. However, these scans are significantly inferior in the detection of several individual CT findings of COVID-19 pneumonia, particularly the ones with GGO. Therefore, they should not be used as the protocol of choice in the initial imaging of COVID-19 patients during which higher sensitivity is required.
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Affiliation(s)
- Hakkı Muammer Karakaş
- Department of Radiology (H.M.K. , G.Y., E.D.Ç.), University of Health Sciences, Istanbul Fatih Sultan Mehmet Training and Research Hospital, Istanbul, Turkey
| | - Gülşah Yıldırım
- Department of Radiology (H.M.K. , G.Y., E.D.Ç.), University of Health Sciences, Istanbul Fatih Sultan Mehmet Training and Research Hospital, Istanbul, Turkey
| | - Esin Derin Çiçek
- Department of Radiology (H.M.K. , G.Y., E.D.Ç.), University of Health Sciences, Istanbul Fatih Sultan Mehmet Training and Research Hospital, Istanbul, Turkey
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Atlı E, Uyanık SA, Öğüşlü U, Cenkeri HÇ, Yılmaz B, Gümüş B. The feasibility of low dose chest CT acquisition protocol for the imaging of COVID-19 pneumonia. Curr Med Imaging 2021; 18:38-44. [PMID: 34165410 DOI: 10.2174/1573405617666210623124108] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/25/2021] [Accepted: 04/08/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To investigate the feasibility of low dose chest CT acquisition protocol for the imaging of either the confirmed case of COVID-19 disease or the suspected case of this disease in adults. METHOD In this retrospective case-control study, the study group consisted of 141 patients who were imaged with low dose chest CT acquisition protocol. The control group consisted of 92 patients who were imaged with the standard protocol. Anteroposterior and lateral diameters of chest, effective diameter and scan length, qualitative and quantitative noise levels, volumetric CT dose index (CTDIvol), dose length product (DLP), and size-specific dose estimations were compared between groups. RESULTS Radiation dose reduction by nearly 90% (CTDIvol and DLP values 1.06 mGy and 40.3 mGy.cm vs. 8.07 mGy and 330 mGy.cm, p < 0.001, respectively) was achieved with the use of low dose acquisition chest CT protocol. Despite higher image noise with low dose acquisition protocol, no significant effect on diagnostic confidence was encountered. Cardiac and diaphragm movement-related artifacts were similar in both groups (p = 0.275). Interobserver agreement was very good in terms of diagnostic confidence assessment. CONCLUSION For the imaging of either the confirmed case of COVID-19 related pneumonia or the suspected case of this disease in adults, low dose chest CT acquisition protocol provides remarkable radiation dose reduction without adversely affecting image quality and diagnostic confidence.
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Affiliation(s)
- Eray Atlı
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
| | - Sadık Ahmet Uyanık
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
| | - Umut Öğüşlü
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
| | - Halime Çevik Cenkeri
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
| | - Birnur Yılmaz
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
| | - Burçak Gümüş
- İstanbul Okan University Hospital, Department of Radiology, Tuzla/İstanbul, Turkey
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Cavli B, Ozturk C, Senel HE, Pekar RB, Elshami W, Tekin HO. Coronavirus Disease 2019 Strategies, Examination Details, and Safety Procedures for Diagnostic Radiology Facilities: An Extensive Multicenter Experience in Istanbul, Turkey. JOURNAL OF RADIOLOGY NURSING 2021; 40:172-178. [PMID: 33398232 PMCID: PMC7773319 DOI: 10.1016/j.jradnu.2020.12.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
This study aimed to share our experiences during the coronavirus disease 2019 (COVID-19) pandemic obtained in diagnostic radiology facilities of 5 training research hospitals in the Asian part of Istanbul (North Hospitals). Accordingly, we reported the used examination details, allocation of radiology staff and actions, and safety procedures for patients and radiology staff. As the corporate radiology team serving in these designated pandemic hospitals, examination details and safety procedures of some diagnostic radiology facilities among 5 training research hospitals have been identified in the current study. Our guidelines and preparedness protocol aimed to reduce patient morbidity and infection-related mortality through quick and proper diagnosis to prevent the spread of COVID-19 to our employees, patients, and the general public during the COVID-19 pandemic. Results showed that teamwork is a key factor while providing medical services. In addition, continuous communication efforts and individual responsibilities of radiology staff were remarkable during the COVID-19 pandemic. The recent situation also showed that co-operation of radiology facilities with device manufacturers and applicators is quite significant especially for development of special protocols in the frame of As Low As Reasonably Achievable. The COVID-19 pandemic has tackled several challenges in radiology among radiology departments. Therefore, continuous co-operation plans and motivational actions are highly recommended not only between radiology staff but also between radiology stakeholders and service providers in the future. Technical details of recent investigation can provide useful information about the management of diagnostic radiology departments during the fight with the COVID-19 pandemic in cities with high population density such as Istanbul.
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Affiliation(s)
| | | | | | | | - Wiam Elshami
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Huseyin Ozan Tekin
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Uskudar University, Medical Radiation Research Center (USMERA), Istanbul, Turkey
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Finance J, Zieleskewicz L, Habert P, Jacquier A, Parola P, Boussuges A, Bregeon F, Eldin C. Low Dose Chest CT and Lung Ultrasound for the Diagnosis and Management of COVID-19. J Clin Med 2021; 10:jcm10102196. [PMID: 34069557 PMCID: PMC8160936 DOI: 10.3390/jcm10102196] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has provided an opportunity to use low- and non-radiating chest imaging techniques on a large scale in the context of an infectious disease, which has never been done before. Previously, low-dose techniques were rarely used for infectious diseases, despite the recognised danger of ionising radiation. METHOD To evaluate the role of low-dose computed tomography (LDCT) and lung ultrasound (LUS) in managing COVID-19 pneumonia, we performed a review of the literature including our cases. RESULTS Chest LDCT is now performed routinely when diagnosing and assessing the severity of COVID-19, allowing patients to be rapidly triaged. The extent of lung involvement assessed by LDCT is accurate in terms of predicting poor clinical outcomes in COVID-19-infected patients. Infectious disease specialists are less familiar with LUS, but this technique is also of great interest for a rapid diagnosis of patients with COVID-19 and is effective at assessing patient prognosis. CONCLUSIONS COVID-19 is currently accelerating the transition to low-dose and "no-dose" imaging techniques to explore infectious pneumonia and their long-term consequences.
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Affiliation(s)
- Julie Finance
- IRD, APHM, MEPHI, IHU Méditerranée Infection, Aix Marseille University, 13005 Marseille, France; (J.F.); (F.B.)
- Service des Explorations Fonctionnelles Respiratoires, APHM, 13005 Marseille, France
| | - Laurent Zieleskewicz
- Department of Anaesthesiology and Intensive Care Medicine, Hôpital Nord, APHM, Aix Marseille Université, 13005 Marseille, France;
- INRA, INSERM, Centre for Cardiovascular and Nutrition Research (C2VN), Aix Marseille Université, 13005 Marseille, France;
| | - Paul Habert
- Service de Radiologie Cardio-Thoracique, Hôpital La Timone, APHM, 13005 Marseille, France; (P.H.); (A.J.)
- LIIE, Aix Marseille University, 13005 Marseille, France
| | - Alexis Jacquier
- Service de Radiologie Cardio-Thoracique, Hôpital La Timone, APHM, 13005 Marseille, France; (P.H.); (A.J.)
- CNRS, CRMBM-CEMEREM (Centre de Résonance Magnétique Biologique et Médicale—Centre d’Exploration Métaboliques par Résonance Magnétique), APHM, Aix-Marseille University, UMR 7339, 13005 Marseille, France
| | - Philippe Parola
- IRD, APHM, SSA, VITROME, Aix Marseille University, 13005 Marseille, France;
- IHU-Méditerranée Infection, Aix Marseille University, 13005 Marseille, France
| | - Alain Boussuges
- INRA, INSERM, Centre for Cardiovascular and Nutrition Research (C2VN), Aix Marseille Université, 13005 Marseille, France;
| | - Fabienne Bregeon
- IRD, APHM, MEPHI, IHU Méditerranée Infection, Aix Marseille University, 13005 Marseille, France; (J.F.); (F.B.)
- Service des Explorations Fonctionnelles Respiratoires, APHM, 13005 Marseille, France
| | - Carole Eldin
- IRD, APHM, SSA, VITROME, Aix Marseille University, 13005 Marseille, France;
- IHU-Méditerranée Infection, Aix Marseille University, 13005 Marseille, France
- Correspondence:
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The Usefulness of Chest CT Imaging in Patients With Suspected or Diagnosed COVID-19: A Review of Literature. Chest 2021; 160:652-670. [PMID: 33861993 PMCID: PMC8056836 DOI: 10.1016/j.chest.2021.04.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 pandemic has had devastating medical and economic consequences globally. The severity of COVID-19 is related, in a large measure, to the extent of pulmonary involvement. The role of chest CT imaging in the management of patients with COVID-19 has evolved since the onset of the pandemic. Specifically, the description of CT scan findings, use of chest CT imaging in various acute and subacute settings, and its usefulness in predicting chronic disease have been defined better. We performed a review of published data on CT scans in patients with COVID-19. A summary of the range of imaging findings, from typical to less common abnormalities, is provided. Familiarity with these findings may facilitate the diagnosis and management of this disease. A comparison of sensitivity and specificity of chest CT imaging with reverse-transcriptase polymerase chain reaction testing highlights the potential role of CT imaging in difficult-to-diagnose cases of COVID-19. The usefulness of CT imaging to assess prognosis, to guide management, and to identify acute pulmonary complications associated with SARS-CoV-2 infection is highlighted. Beyond the acute stage, it is important for clinicians to recognize pulmonary parenchymal abnormalities, progressive fibrotic lung disease, and vascular changes that may be responsible for persistent respiratory symptoms. A large collection of multi-institutional images were included to elucidate the CT scan findings described.
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Kasper J, Decker J, Wiesenreiter K, Römmele C, Ebigbo A, Braun G, Häckel T, Schwarz F, Wehler M, Messmann H, Kröncke TJ, Scheurig-Münkler C. Typical Imaging Patterns in COVID-19 Infections of the Lung on Plain Chest Radiographs to Aid Early Triage. ROFO-FORTSCHR RONTG 2021; 193:1189-1196. [PMID: 33694145 DOI: 10.1055/a-1388-8147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
PURPOSE To evaluate imaging patterns of a COVID-19 infection of the lungs on chest radiographs and their value in discriminating this infection from other viral pneumonias. MATERIALS AND METHODS All 321 patients who presented with respiratory impairment suspicious for COVID-19 infection between February 3 and May 8, 2020 and who received a chest radiograph were included in this analysis. Imaging findings were classified as typical for COVID-19 (bilateral, peripheral opacifications/consolidations), non-typical (findings consistent with lobar pneumonia), indeterminate (all other distribution patterns of opacifications/consolidations), or none (no opacifications/consolidations). The sensitivity, specificity, as well as positive and negative predictive value for the diagnostic value of the category "typical" were determined. Chi² test was used to compare the pattern distribution between the different types of pneumonia. RESULTS Imaging patterns defined as typical for COVID-19 infections were documented in 35/111 (31.5 %) patients with confirmed COVID-19 infection but only in 4/210 (2 %) patients with any other kind of pneumonia, resulting in a sensitivity of 31.5 %, a specificity of 98.1 %, and a positive and negative predictive value of 89.7 % or 73 %, respectively. The sensitivity could be increased to 45.9 % when defining also unilateral, peripheral opacifications/consolidations with no relevant pathology contralaterally as consistent with a COVID-19 infection, while the specificity decreases slightly to 93.3 %. The pattern distribution between COVID-19 patients and those with other types of pneumonia differed significantly (p < 0.0001). CONCLUSION Although the moderate sensitivity does not allow the meaningful use of chest radiographs as part of primary screening, the specific pattern of findings in a relevant proportion of those affected should be communicated quickly as additional information and trigger appropriate protective measures. KEY POINTS · COVID-19 infections show specific X-ray image patterns in 1/3 of patients.. · Bilateral, peripheral opacities and/or consolidations are typical imaging patterns.. · Unilateral, peripheral opacities and/or consolidations should also raise suspicion of COVID-19 infection.. CITATION FORMAT · Kasper J, Decker J, Wiesenreiter K et al. Typical Imaging Patterns in COVID-19 Infections of the Lung on Plain Chest Radiographs to Aid Early Triage. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1388-8147.
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Affiliation(s)
- Judith Kasper
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Josua Decker
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Katharina Wiesenreiter
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Christoph Römmele
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Georg Braun
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Thomas Häckel
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Florian Schwarz
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Markus Wehler
- Department of Emergency Medicine and Department of Medicine IV, University Hospital Augsburg, Germany
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Thomas J Kröncke
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
| | - Christian Scheurig-Münkler
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
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Abd-Alrazaq A, Schneider J, Mifsud B, Alam T, Househ M, Hamdi M, Shah Z. A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis. J Med Internet Res 2021; 23:e23703. [PMID: 33600346 PMCID: PMC7942394 DOI: 10.2196/23703] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 10/14/2020] [Accepted: 11/24/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. OBJECTIVE We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. RESULTS Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.
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Affiliation(s)
- Alaa Abd-Alrazaq
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Jens Schneider
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Borbala Mifsud
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Tanvir Alam
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mounir Hamdi
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Shiri I, Akhavanallaf A, Sanaat A, Salimi Y, Askari D, Mansouri Z, Shayesteh SP, Hasanian M, Rezaei-Kalantari K, Salahshour A, Sandoughdaran S, Abdollahi H, Arabi H, Zaidi H. Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network. Eur Radiol 2021; 31:1420-1431. [PMID: 32879987 PMCID: PMC7467843 DOI: 10.1007/s00330-020-07225-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. METHODS In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). RESULTS The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. CONCLUSIONS The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. KEY POINTS • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran
| | - Zahra Mansouri
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Saleh Sandoughdaran
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications. Clin Imaging 2021; 77:151-157. [PMID: 33684789 PMCID: PMC7906537 DOI: 10.1016/j.clinimag.2021.02.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/29/2021] [Accepted: 02/18/2021] [Indexed: 12/16/2022]
Abstract
As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.
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Butt FK, Julian K, Kadry Z, Jain A. Navigating Kidney Transplantation in the Early Phase of Coronavirus Disease 2019: Screening Patients With Reverse Transcriptase Polymerase Chain Reaction and Low-Radiation-Dose Chest Computed Tomography. Transplant Proc 2021; 53:1169-1174. [PMID: 33518290 PMCID: PMC7796654 DOI: 10.1016/j.transproceed.2021.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 12/31/2020] [Accepted: 01/05/2021] [Indexed: 11/04/2022]
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic of 2020 changed organ transplantation. All elective cases at our institution were postponed for approximately 3 months. Centers for Medicare and Medicaid Services considers organ transplant surgery a Tier 3b case, along with other high acuity procedures, recommending no postponement. Our transplant program collaborated with our transplant infectious disease colleagues to create a protocol that would ensure both patient and staff safety during these unprecedented times. Methods The living donor program was electively placed on hold until we had the proper protocols in place. Preoperative COVID-19 testing was required for all recipients and living donors. All patients underwent a rapid nasopharyngeal swab test. After testing negative by nasopharyngeal swab, recipients also underwent a low-radiation-dose computed tomography scan to rule out any radiographic changes suggestive of a COVID-19 infection. Results We performed 8 living donor and 9 deceased donor kidney transplants. In comparison, we performed 10 living donor and 4 deceased donor transplants during the same time period in the previous year. Our testing protocol enabled efficient use of all suitable organs offered during the viral pandemic. No recipients or living donors tested positive or developed COVID-19. Conclusions Creation of a viral testing protocol, developed in conjunction with our infectious disease team, permitted kidney transplantation to be performed safely, and the number of deceased donor transplants increased considerably without adversely affecting our outcomes.
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Affiliation(s)
- Fauzia K Butt
- Department of Surgery, Division of Transplantation, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania.
| | - Kathleen Julian
- Department of Medicine, Division of Infectious Diseases, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - Zakiyah Kadry
- Department of Surgery, Division of Transplantation, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - Ashokkumar Jain
- Department of Surgery, Division of Transplantation, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania
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