Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Dec 28, 2023; 15(12): 338-349
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.338
Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas
Yukihisa Takayama, Keisuke Sato, Shinji Tanaka, Ryo Murayama, Nahoko Goto, Kengo Yoshimitsu
Yukihisa Takayama, Keisuke Sato, Shinji Tanaka, Ryo Murayama, Nahoko Goto, Kengo Yoshimitsu, Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka 8140180, Japan
Author contributions: Takayama Y and Yoshimitsu K designed the research and wrote the paper; Sato K, Tanaka S, Murayama R, and Goto N contributed to data collection, data analysis, and all authors approved the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Fukuoka University [Approval No. U21-966].
Informed consent statement: The requirement for written informed consent was waived because this was a retrospective analysis of image post-processing of clinical MR data.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data is available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yukihisa Takayama, MD, PhD, Associate Professor, Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka 8140180, Japan. ytakayama@fukuoka-u.ac.jp
Received: October 22, 2023
Peer-review started: October 22, 2023
First decision: November 2, 2023
Revised: November 12, 2023
Accepted: December 4, 2023
Article in press: December 4, 2023
Published online: December 28, 2023
Processing time: 64 Days and 3.8 Hours
Abstract
BACKGROUND

It has been reported that deep learning-based reconstruction (DLR) can reduce image noise and artifacts, thereby improving the signal-to-noise ratio and image sharpness. However, no previous studies have evaluated the efficacy of DLR in improving image quality in reduced-field-of-view (reduced-FOV) diffusion-weighted imaging (DWI) [field-of-view optimized and constrained undistorted single-shot (FOCUS)] of the pancreas. We hypothesized that a combination of these techniques would improve DWI image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.

AIM

To evaluate the efficacy of DLR for image quality improvement of FOCUS of the pancreas.

METHODS

This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021. We evaluated three types of FOCUS examinations: FOCUS with DLR (FOCUS-DLR+), FOCUS without DLR (FOCUS-DLR−), and conventional FOCUS (FOCUS-conv). The three types of FOCUS and their apparent diffusion coefficient (ADC) maps were compared qualitatively and quantitatively.

RESULTS

FOCUS-DLR+ (3.62, average score of two radiologists) showed significantly better qualitative scores for image noise than FOCUS-DLR− (2.62) and FOCUS-conv (2.88) (P < 0.05). Furthermore, FOCUS-DLR+ showed the highest contrast ratio (CR) between the pancreatic parenchyma and adjacent fat tissue for b-values of 0 and 600 s/mm2 (0.72 ± 0.08 and 0.68 ± 0.08) and FOCUS-DLR− showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2 (0.62 ± 0.21 and 0.62 ± 0.21) (P < 0.05), respectively. FOCUS-DLR+ provided significantly higher ADCs of the pancreas and lesion (1.44 ± 0.24 and 3.00 ± 0.66) compared to FOCUS-DLR− (1.39 ± 0.22 and 2.86 ± 0.61) and significantly lower ADCs compared to FOCUS-conv (1.84 ± 0.45 and 3.32 ± 0.70) (P < 0.05), respectively.

CONCLUSION

This study evaluated the efficacy of DLR for image quality improvement in reduced-FOV DWI of the pancreas. DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution. The denoising level of DWI can be controlled to make the images appear more natural to the human eye. However, this study revealed that DLR did not ameliorate pancreatic distortion. Additionally, physicians should pay attention to the interpretation of ADCs after DLR application because ADCs are significantly changed by DLR.

Keywords: Deep learning-based reconstruction; Magnetic resonance imaging; Reduced field-of-view; Diffusion-weighted imaging; Pancreas

Core Tip: This study evaluated the efficacy of deep learning-based reconstruction (DLR) for image quality improvement in reduced-field-of-view diffusion-weighted imaging (DWI) of the pancreas. DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution. The denoising level of DWI can be controlled to make the images appear more natural to the human eye. However, this study revealed that DLR did not ameliorate pancreatic distortion. Additionally, physicians should pay attention to the interpretation of apparent diffusion coefficients (ADCs) after DLR application because ADCs are significantly changed by DLR.