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Abstract #1746

Enhancing Real-Time Cardiac MRI with Image-Domain Diffusion Model for Arrhythmia Patient

Jessie Dong1, Trevor Chan1, Yuchi Han2, and Walter Witschey1
1University of Pennsylvania, Philadelphia, PA, United States, 2Ohio State University, Columbus, OH, United States

Synopsis

Keywords: AI Diffusion Models, Arrhythmia, Non-cartesian sampling, radial

Motivation: Persistent premature ventricular obscure left ventricle (LV) function assessment. Current imaging lacks temporal resolution and methods for effective differentiation of beat morphologies.

Goal(s): To enhance real-time MRI scans of arrhythmia patients, targeting high temporal resolution for discerning arrhythmia beats.

Approach: We trained an image-domain diffusion model on a public database, optimizing transferability to arrhythmia scans. The model employs prior images during the reverse sampling to impose image-domain constraints.


Results: Achieved a 62% increase in LV signal-to-noise ratio and a 150% increase in LV-to-myocardium contrast-to-noise ratio across 10 real-time scans. Also facilitated direct beat morphology analysis, paving the way for PVC-induced cardiomyopathy studies.

Impact: The trajectory-agnostic diffusion model offers clinicians and patients clearer visualization of real-time arrhythmia scans, potentially assisting the early detection and study of PVC-induced cardiomyopathies. Future research may explore its applicability to other rapid-cycle cardiac phenomena.

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Keywords