Keywords: Myocardium, Diffusion Denoising
Motivation: This study addresses critical limitations in myocardial perfusion MRI, specifically restricted slice coverage and temporal resolution, which are essential for accurate perfusion assessment.
Goal(s): To enhance myocardial perfusion MRI by achieving high slice coverage and temporal resolution, enabling real-time inline imaging display using low-resolution images for smoother workflow.
Approach: We developed PerfGen, a conditional diffusion-based generative model that, combined with GRAPPA, achieves 6–9-fold acceleration by generating high-resolution images from low-resolution acquisitions.
Results: Trained and validated on clinical rest and stress datasets, PerfGen reduced nRMSE by 20%, increased PSNR by 6%, and improved SSIM by 3%, complementing GRAPPA or other reconstruction methods.
Impact: PerfGen enables high-quality myocardial perfusion MRI, complementing GRAPPA or similar methods to improve imaging speed with real-time displays. This conditional diffusion-based super-resolution approach generates multiple contrast phases and complements GRAPPA or similar methods, further advancing myocardial perfusion imaging.
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