Keywords: Image Reconstruction, Perfusion
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.
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