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Abstract #3196

SOJU-Net—Denoising MR phase images with physics-informed deep learning using artificial Rician noise augmentation 

Thomas Jochmann1, Nora Kuechler1, Jens Haueisen1, and Ferdinand Schweser2,3
1Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, 2Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, 3Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Phase noise follows the Rician distribution, with a non-linear dependency of the phase noise on the local magnitude signal intensity. In this work, we present SOJU-Net, a deep-learning based denoising for MR phase images. SOJU-Net reduces Rician noise while preserving boundary contrast.

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