Accelerated Cardiac MRI Using Deep Learning Cine Imaging: Validation in Pediatric Patients both with and without Congenital Heart Disease

BACKGROUND: Conventional breath-held 2D bSSFP cine CMR requires multiple breath holds and prolonged acquisition time, posing challenges for children and patients with congenital heart disease (CHD). Deep-learning (DL)-based reconstruction enables undersampled data acquisition with rapid image completion, offering the potential for substantial time savings while preserving diagnostic accuracy. METHODS: Fifty pediatric patients (median age 16.1 years [14.7, 17.5], 56% CHD) with two-ventricle physiology were prospectively recruited, and underwent conventional breath-held bSSFP cine imaging and DL-accelerated cine imaging at three acceleration levels: with breath-held acquisitions over 3-RR intervals, 1-RR interval, and 1-RR interval with free-breathing. Acquisition time, image quality, and quantitative ventricular measurements were compared across sequences. RESULTS: Median short-axis cine stack acquisition time was reduced by 66%, 83%, and 89% for the 3-RR, 1-RR, and 1-RR free-breathing sequences, respectively, compared with conventional cine (all p<0.001). Image quality remained diagnostic in 96%, 88%, and 88% of cases, respectively with no statistical difference in image quality score between conventional cine and 3-RR acquisition. Quantitative assessment of myocardial thickening via radial strain demonstrated excellent agreement (ICC = 0.92, 95% CI 0.82-0.96, p < 0.001) between conventional cine and 3-RR acquisition, with only slightly higher measurements. on 3-RR sequences (29.7 ± 6.5% vs. 28.5 ± 6.1%, p<0.001). LV and RV volumes differed by <5 and <10mL/m² across all acceleration levels, LV mass by <3g/m², and LVEF/RVEF by ≤2%. Among CHD patients, a 2% overestimation of RVEF was observed. CONCLUSIONS: DL Cine enables rapid and diagnostically robust quantification of biventricular size and function in pediatric patients, including those with CHD. Image acceleration with 3-RR acquisitions optimally balances efficiency and image quality, supporting integration of DL cine imaging into routine pediatric and congenital CMR workflows.