Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients

作者：Huaijun Wang, Anne H. Schmieder, Mary P. Watkins, Pengjun Wang, Joshua D. Mitchell, Syed Z. Qamer, Gregory Lanza · 发表于：World Journal of Cardiology · 年份：2025 · DOI：10.4330/wjc.v17.i7.108745 · 被引用次数：4 · 研究领域：Cardiac Imaging and Diagnostics、Takotsubo Cardiomyopathy and Associated Phenomena、ECG Monitoring and Analysis

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 reli...