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Abstract #1951

A Custom Loss Function for Deep Learning-Based Brain MRI Reconstruction

Abhinav Saksena1, Makarand Parigi1, Nicole Seiberlich2, and Yun Jiang2
1EECS, University of Michigan, Ann Arbor, MI, United States, 2Department of Radiology, University of Michigan, Ann Arbor, MI, United States

The purpose of this work is to test and evaluate a number of candidate loss functions for the reconstruction of diagnostic quality brain MRI images using undersampled k-space data and CNNs. We investigate both per-pixel (L1) and perceptual based (SSIM) loss functions, before developing a custom loss function that incorporates elements of both. We train these loss functions implemented in a UNet architecture on both 4x and 8x undersampled 16-coil MRI data. The custom loss function is shown to produce both the best quantitative results and also sharper and more detailed reconstructions across a number of image contrasts.

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