Abstract #0644

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

Kerstin Hammernik¹, Florian Knoll^2,3, Daniel K Sodickson^2,3, and Thomas Pock^1,4

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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