Prospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Cardiol. Jul 26, 2025; 17(7): 108745
Published online Jul 26, 2025. doi: 10.4330/wjc.v17.i7.108745
Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients
Huaijun Wang, Anne Schmieder, Mary Watkins, Pengjun Wang, Joshua Mitchell, S Zyad Qamer, Gregory Lanza
Huaijun Wang, United Imaging Healthcare, Houston, TX 77054, United States
Anne Schmieder, Mary Watkins, Pengjun Wang, Joshua Mitchell, S Zyad Qamer, Gregory Lanza, Division of Cardiology, Washington University in Saint Louis, Saint Louis, MO 63108, United States
Author contributions: Wang H, Schmieder A, Watkins M, and Lanza G designed the study; Wang H, Schmieder A, Watkins M, Wang P, Micthell J, Qamaer SZ, and Lanza G performed the study; Wang H and Lanza G wrote the manuscript; Wang H and Schmieder A created the figures. All authors approved the manuscript.
Supported by James Russell Hornsby and Jun Xiong Fund and United Imaging Healthcare.
Institutional review board statement: The study was approved by Washington University in Saint Louis Human Research Protection Office, with IRB ID number 202003199.
Informed consent statement: All participants provided written informed consent to participate after being fully informed about the study’s objectives, procedures, potential risks, benefits, and confidentiality measures.
Conflict-of-interest statement: Dr. Lanza reports research MRI support (equipment, service, and technical collaboration) from United Imaging Healthcare.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: All data generated or analyzed during this study are included in this published article. Additional data related to this research are available from the corresponding author upon reasonable request.
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: Gregory Lanza, MD, PhD, Division of Cardiology, Washington University in Saint Louis, Cortex One Building 4320 Forest Park Ave, Saint Louis, MO 63108, United States. gmlanza@wustl.edu
Received: April 24, 2025
Revised: June 4, 2025
Accepted: July 1, 2025
Published online: July 26, 2025
Processing time: 89 Days and 13.9 Hours
Abstract
BACKGROUND

A key cardiac magnetic resonance (CMR) challenge is breath-holding duration, difficult for cardiac patients.

AIM

To evaluate whether artificial intelligence-assisted compressed sensing CINE (AI-CS-CINE) reduces image acquisition time of CMR compared to conventional CINE (C-CINE).

METHODS

Cardio-oncology patients (n = 60) and healthy volunteers (n = 29) underwent sequential C-CINE and AI-CS-CINE with a 1.5-T scanner. Acquisition time, visual image quality assessment, and biventricular metrics (end-diastolic volume, end-systolic volume, stroke volume, ejection fraction, left ventricular mass, and wall thickness) were analyzed and compared between C-CINE and AI-CS-CINE with Bland–Altman analysis, and calculation of intraclass coefficient (ICC).

RESULTS

In 89 participants (58.5 ± 16.8 years, 42 males, 47 females), total AI-CS-CINE acquisition and reconstruction time (37 seconds) was 84% faster than C-CINE (238 seconds). C-CINE required repeats in 23% (20/89) of cases (approximately 8 minutes lost), while AI-CS-CINE only needed one repeat (1%; 2 seconds lost). AI-CS-CINE had slightly lower contrast but preserved structural clarity. Bland-Altman plots and ICC (0.73 ≤ r ≤ 0.98) showed strong agreement for left ventricle (LV) and right ventricle (RV) metrics, including those in the cardiac amyloidosis subgroup (n = 31). AI-CS-CINE enabled faster, easier imaging in patients with claustrophobia, dyspnea, arrhythmias, or restlessness. Motion-artifacted C-CINE images were reliably interpreted from AI-CS-CINE.

CONCLUSION

AI-CS-CINE accelerated CMR image acquisition and reconstruction, preserved anatomical detail, and diminished impact of patient-related motion. Quantitative AI-CS-CINE metrics agreed closely with C-CINE in cardio-oncology patients, including the cardiac amyloidosis cohort, as well as healthy volunteers regardless of left and right ventricular size and function. AI-CS-CINE significantly enhanced CMR workflow, particularly in challenging cases. The strong analytical concordance underscores reliability and robustness of AI-CS-CINE as a valuable tool.

Keywords: Cardiac magnetic resonance; CINE imaging; Artificial intelligence; Compressed sensing; Imaging workflow; Acquisition time; Cardiac function; Cardio-oncology; Image quality; Challenging patients

Core Tip: In this prospective study of 89 patients and volunteers, we demonstrate that artificial-intelligence-assisted compressed sensing (AI-CS-CINE) significantly streamlines cardiac magnetic resonance imaging workflows, reducing acquisition time by 84% (37 seconds vs 238 seconds) compared to conventional CINE imaging. Quantitative analysis showed excellent agreement in biventricular volumes and function (intraclass correlation coefficient 0.73-0.98). AI-CS-CINE proved especially valuable in challenging cases, such as for patients with cardiac amyloidosis, enabling faster acquisition and more reliable interpretation. These findings highlight AI-CS-CINE as a robust, time-efficient alternative to conventional methods, with potential to improve clinical efficiency and image quality in diverse cardiac populations.