Deep Learning-Based Breath-Hold and Free-Breathing Cine MRI for Comprehensive Cardiac Evaluation.

objectiveTo evaluate and compare scan times, measurement accuracy, and image quality (IQ) of free-breathing (FB) and breath-hold (BH) deep learning (DL) cine MRI sequences versus standard cine MRI, with a specific focus on patients with arrhythmia and dyspnea. MATERIALS AND

methodsSeventy participants were prospectively enrolled, including 24 with arrhythmia, 17 with dyspnea, and 29 with normal sinus rhythm and eupnea (mean age, 49 ± 17 years). Each patient underwent three cine MRI acquisitions (standard cine, BHDL, and FBDL) on a 3T scanner. Quantitative assessments of biventricular function, left ventricular mass, and myocardial strain were independently performed by three radiologists, blinded to image acquisition techniques. IQ was evaluated by the same readers using both a five-point Likert scale and objective metrics.

resultsBoth BHDL and FBDL significantly reduced total examination times compared to standard cine (BHDL: 58 ± 5 s; FBDL: 88 ± 12 s; standard cine: 208 ± 12 s; adjusted

conclusionBoth FBDL and BHDL significantly reduced scan times compared to standard cine without compromising quantitative measurement accuracy. BHDL offered superior measurement accuracy and shorter scan time than FBDL. Furthermore, BHDL demonstrated robust suitability for patients with arrhythmia by minimizing arrhythmia-related artifacts, whereas FBDL was more effective in patients with dyspnea by mitigating respiratory motion artifacts.