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Abstract #1295

Contrast-weighted SSIM loss function for deep learning-based undersampled MRI reconstruction

Sangtae Ahn1, Anne Menini2, Graeme McKinnon3, Erin M. Gray4, Joshua D. Trzasko4, John Huston4, Matt A. Bernstein4, Justin E. Costello5, Thomas K. F. Foo1, and Christopher J. Hardy1
1GE Research, Niskayuna, NY, United States, 2GE Healthcare, Menlo Park, CA, United States, 3GE Healthcare, Waukesha, WI, United States, 4Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, United States, 5Walter Reed National Military Medical Center, Bethesda, MD, United States

Deep learning-based undersampled MRI reconstructions can result in visible blurring, with loss of fine detail. We investigate here various structural similarity (SSIM) based loss functions for training a compressed-sensing unrolled iterative reconstruction, and their impact on reconstructed images. The conventional unweighted SSIM has been used both as a loss function, and, more generally, for assessing perceived image quality in various applications. Here we demonstrate that using an appropriately weighted SSIM for the loss function yields better reconstruction of small anatomical features compared to L1 and conventional SSIM loss functions, without introducing image artifacts.

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