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Abstract #3517

Noise2DWI: Accelerating Diffusion Tensor Imaging with Self-Supervision and Fine Tuning

Phillip Martin1, Maria Altbach2,3, and Ali Bilgin1,2,3,4
1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Biomedical Engineering, University of Arizona, Tucson, AZ, United States, 3Medical Imaging, University of Arizona, Tucson, AZ, United States, 4Applied Mathematics, University of Arizona, Tucson, AZ, United States

Synopsis

In this work we propose a novel algorithm of denoising accelerated diffusion weighted MRI (dMRI) acquisitions using deep learning and self-supervision. This method effectively enables the prediction of diffusion-weighted images (DWIs), without the need for large amounts of training data with high directional encodings. We demonstrate that accurate diffusion tensor metrics can be obtained with as few as 6 DWIs using only a few training datasets with high directional encodings.

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