Keywords: Analysis/Processing, Segmentation, Heart, Image Reconstruction, AI/ML Software, Data Analysis, Data Processing, Diagnosis/Prediction
Motivation: CMR is the gold standard for evaluating cardiac function, but its lengthy acquisition and dependence on expert readers limit its efficiency.
Goal(s): We aim to develop an automated accelerated cardiac function assessment method that requires minimal data acquisition and human intervention.
Approach: We propose an efficient framework for cardiac function assessment, trained on sparsely labeled, accelerated images. Our multi-task method employs a synergistic loop of registration, motion-compensated reconstruction, and segmentation, enabling mutual refinement.
Results: We demonstrate reliable ventricular function analysis from accelerated MRI data, acquired even within single breath-hold Cine, and achieved a 13%-25% improvement in Dice similarity over other deep learning-based methods.
Impact: Our framework enables automated cardiac function assessment, even for highly accelerated single breath-hold scans. We improve CMR accessibility for studies with limited subjects and sparse manual annotations. Results indicate reliable motion estimation, ventricular function measures and myocardial strain analysis.
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