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Abstract #4342

Conditional Diffusion-Generated Super-Resolution for Myocardial First-Pass Perfusion MRI

Changyu Sun1,2, Neha Goyal3, Darla Tharp4, Senthil Kumar3, and Talissa Altes2
1Department of Chemical and Biomedical Engineering, University of Missouri-Columbia, Columbia, MO, United States, 2Department of Radiology, University of Missouri-Columbia, Columbia, MO, United States, 3Department of Medicine, University of Missouri-Columbia, Columbia, MO, United States, 4Department of Biomedical Sciences, University of Missouri-Columbia, Columbia, MO, United States

Synopsis

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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