High-Resolution Free-breathing Perfusion MRI with High Slice Coverage via SAM in Self-Supervised Learning with Self-Regularization

Motivation: Enhancing myocardial perfusion MRI with self-supervised learning is key to achieving higher image quality and fidelity, especially in patients with varying image matrix sizes and asymmetric echo. Goal(s): To enhance perfusion MRI by increasing resolution and slice coverage using self-supervised learning, self-regularization, and spatial attention, tailored for varied image sizes and asymmetric echo. Approach: Implemented an accelerated perfusion MRI sequence with asymmetric echo; collected data from 20 patients; developed self-LR with SAM to enhance image quality. Results: Self-LR with SAM yielded superior image quality and fewer artifacts in varied sizes and asymmetric echo, outperforming other methods, confirmed by expert evaluations. Impact: The integration of Spatial Attention Module (SAM) with Self-Supervised Learning and Self-Regularization significantly enhances myocardial perfusion MRI, enriching spatial resolution and slice coverage. This development could potentially improve diagnostic accuracy, facilitating non-invasive whole-heart assessments with improved image quality.