Abstract #2920

2.5D Networks for Physics-Guided Deep Learning Reconstruction of 3D Non-Cartesian MRI from Limited Training Data

Chi Zhang^1,2, Davide Piccini^3,4, Omer Burak Demirel^1,2, Gabriele Bonanno⁴, Steen Moeller², Burhaneddin Yaman^1,2, Matthias Stuber^3,5, and Mehmet Akçakaya^1,2

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 Healthineers International, Lausanne, Switzerland, ⁵Center for Biomedical Imaging, Lausanne, Switzerland

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceAlthough recent studies enabled physics-guided deep learning (PG-DL) reconstruction of 3D non-Cartesian MRI, it suffers from blurring, partially due to limited training data. In this study we propose 2.5D PG-DL using three 2D CNNs on orthogonal views for 3D reconstruction to efficiently exploit the limited training data. Results on 3D kooshball coronary MRI show the proposed strategy noticeably improves image sharpness.

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