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Abstract #0644

On the Influence of Sampling Pattern Design on Deep Learning-Based MRI Reconstruction

Kerstin Hammernik1, Florian Knoll2,3, Daniel K Sodickson2,3, and Thomas Pock1,4

1Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 2Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 3Department of Radiology, NYU School of Medicine, New York, NY, United States, 4Safety & Security Department, AIT Austrian Institute of Technology GmbH, Vienna, Austria

In this work, we address the question if variable density sampling of 2D Cartesian knee sequences can improve deep learning-based MRI reconstruction. Our results suggest that incoherent artifacts introduced by variable density sampling are beneficial to reconstruct highly accelerated sequences. Additionally, we show that our learning-based approach for regular sampling improves reconstruction results compared to classical compressed sensing methods with variable density sampling for our target application.

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