Abstract #3517

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

Phillip Martin¹, Maria Altbach^2,3, and Ali Bilgin^1,2,3,4

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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