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Abstract #4867

Distributed memory-efficient deep learning reconstruction for improved SIMBA whole-heart coronary MRI

Chi Zhang1,2, Ludovica Romanin3,4, Davide Piccini3,4, Steen Moeller2, Matthias Stuber3,5, and Mehmet Akçakaya1,2
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 3Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 4Advanced Clinical Imaging Technology, Siemens Healthcare AG, Bern, Switzerland, 5Center for Biomedical Imaging, Lausanne, Switzerland


SImilarity-driven Multi-dimensional Binning Algorithm (SIMBA) is a recently proposed technique for identifying motion-consistent clusters in free-running whole-heart MRA acquisitions, where the clusters are subsequently reconstructed using conventional approaches. Physics-guided deep learning (PG-DL) reconstruction has gained popularity with its superior performance at higher acceleration rates and may further improve the image quality of free-running whole-heart MRA in conjunction with SIMBA. However, PG-DL is difficult to apply to 3D non-Cartesian acquisitions due to hardware limitations. In this work we enable distributed memory-efficient PG-DL for large-scale SIMBA datasets, showing preliminary results in which PG-DL improves image quality compared to conventional methods.

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