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Abstract #0687

L2 or not L2: Impact of Loss Function Design for Deep Learning 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

Human radiologists gain experience from reading numerous MRI images to recognize pathologies and anatomical structures. To integrate this experience into deep learning approaches, two major components are required: We need both a suitable network architecture and a suitable loss function that measures the similarity between the reconstruction and the reference. In this work, we compare pixel-based and patch-based loss functions. We show that it is beneficial to consider other loss functions than the squared L2 norm to get a better representation of the human perceptual system and thus to preserve the texture in the tissue.

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