ABSTRACT Purpose This study aims to develop an automated framework for operator‐independent assessment of cardiac ventricular function from highly accelerated images. Methods We introduce a deep learning framework that generates reliable ventricular volumetric parameters and strain measures from fully sampled and retrospectively accelerated MR images. This method integrates image registration, motion‐compensated reconstruction, and segmentation in a synergetic loop for mutual refinement. The evaluation was performed on an in‐house dataset of healthy and cardiovascular‐diseased subjects. We examined the performance of the underlying tasks, including registration and segmentation, and their impact on derived parameters related to ventricular function. Results The proposed approach demonstrates robustness to undersampling artifacts and requires limited annotation, while still reducing variability and errors for segmentation and registration. This translates to a to increase in Dice similarity compared to existing deep learning methods for left endocardium, left epicardium, and right ventricular delineation. Analysis of the predicted left and right ventricular ejection fraction reveals a strong correlation () with manual measurements. Moreover, the estimated motion and segmentation masks enable consistent radial and circumferential strain measurements across accelerations up to . Conclusion A comprehensive ventricular function analysis can be performed using highly accelerated cine MR data with minimal annotation effort. This multitasking strategy has the potential to enable more accessible cardiac function analysis within a single breath‐hold.
Ghoul et al. (Sat,) studied this question.
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