Abstract #0388

Database-Free Zero-Shot Deep Learning Reconstruction for Highly-Accelerated Free-Breathing Perfusion CMR

Omer Burak Demirel^1,2, Chi Zhang^1,2, Burhaneddin Yaman^1,2, Toygan Kilic^1,2, Steen Moeller², Chetan Shenoy³, Sebastian Weingärtner⁴, Tim Leiner⁵, and Mehmet Akçakaya^1,2

¹Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, ³Department of Medicine (Cardiology), University of Minnesota, Minneapolis, MN, United States, ⁴Delft University of Technology, Delft, Netherlands, ⁵Department of Radiology, Mayo Clinical, Rochester, MN, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceMyocardial perfusion CMR is used to functionally assess coronary artery disease. However, its resolution and coverage remain limited and require rapid imaging .At high accelerations for whole-heart coverage and high spatio-temporal resolution, conventional reconstructions suffer from noise and aliasing artifacts. Physics-guided deep learning (PG-DL) reconstruction has shown improved image quality in fast MRI, but its application to perfusion CMR has been limited due to substantial differences in breathing and contrast uptakes among subjects. In this work, we tackle these challenges by adopting subject-specific self-supervised PG-DL that does not require a training database for simultaneous multi-slice accelerated myocardial perfusion CMR.

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