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Abstract #3434

Self-supervised IVIM DWI parameter estimation with a physics based forward model

Serge Vasylechko1,2, Simon K. Warfield1,2, Onur Afacan1,2, and Sila Kurugol1,2
1Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States

The goal of this study was to assess the robustness and repeatability of intravoxel incoherent motion model (IVIM) parameter estimation for the diffusion weighted MRI in the abdominal organs under the constraints of noisy diffusion signal using a novel neural network training method. The method is based on the principle of a physics guided self-supervised neural network that does not require supervision for training. Such approach is beneficial in conditions where the reference methods are not available, or are not robust enough to provide good supervision. This work is targeting evaluations towards accelerated IVIM DWI scanning which exhibit low SNR.

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