End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI

arXiv:2403.10346v1 Announce Type: cross
Abstract: Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort. Traditional deep learning (DL) approaches for accelerated dynamic MRI reconstruction typically rely on predefined or random subsampling patterns, applied uniformly across all temporal phases. This standard practice overlooks the potential benefits of leveraging temporal correlations and lacks the adaptability required for case-specific subsampling optimization, which holds the potential for maximizing reconstruction quality. Addressing this gap, we present a …

abstract applications arxiv clinical cs.cv deep learning dynamic eess.iv mri patient patterns physics.med-ph practice random standard temporal type