Meeting Banner
Abstract #3482

DKI parameter inference by deep neural networks trained by synthetic data

Ko Sasaki1,2 and Yoshitaka Masutani2

1Radiology, Hiroshima Heiwa Clinic, Hiroshima, Japan, 2Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan

In general, DKI parameters (D and K) are obtained by fitting models to DWI signal values, such as by least-square fitting (LSF) methods. However, when DWI signal values are contaminated by noise of high level, fitting error is often observed especially for diffusional kurtosis K. In this study, we propose a robust method to infer DKI parameters based on deep neural networks trained by only synthetic data to overcome the limitations of real data training. Our experimental results including comparison with LSF showed the potential of our method for robust inference of DKI parameters.

This abstract and the presentation materials are available to members only; a login is required.

Join Here