Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation

Background Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment. Purpose To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k‐space over a range of acceleration factors. Study Type Retrospective. Subjects Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12‐second BH capability. Field Strength/Sequence 1.5‐T, cine bSSFP. Assessment Retrospective DLR was conducted by applying compressed sensitivity encoding (C‐SENSE) acceleration to systematically undersample fully sampled k‐space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k‐space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL‐based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience. Statistical Tests Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal–Wallis test, and Wilcoxon signed‐rank test were employed as appropriate. A P‐value