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Abstract #2238

Deep Learning Artifact Removal for Real-Time Rodent Cardiac MRI

Patrick Metze1, Hao Li2, Tobias Speidel2, Ina Vernikouskaya1, and Volker Rasche1,2
1Department of Internal Medicine II, University Ulm Medical Center, Ulm, Germany, 2Core Facility Small Animal Imaging (CF-SANI), Ulm University, Ulm, Germany

Reconstruction methods incorporating Deep Learning have gained a lot of traction in the recent past. However, most of these methods have been evaluated in human MRI. In this work, we show the feasibility of deep-learning artifact removal for the tiny golden angle radial trajectory in rodent cardiac MRI and validate two approaches against a Compressed Sensing reconstruction and the gated reference standard. The deep-learning based methods achieve acceptable visual image quality and exhibit only slight, but for one method significant, differences in the functional analysis.

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