Self-supervised learning with motion correction for fitting IVIM model parameters

Motivation: The existing fitting methods of lntravoxel incoherent motion (IVI M) parameter maps can already achieve good performance, but they all ignore the impact of internal heart beat or breathing movements on multiple b-value images for the fitting performance. Goal(s): We expect to incorporate motion correction in the fitting process to improve fitting performance. Approach: In this study, we propose an end-to-end deep network structure that combines self-supervised learning and motion correction for fitting IVIM parameters. Results: The quantitative and qualitative comparative experimental results indicate that using self-supervised motion correction can improve the fitting performance. Impact: For the first time, we consider incorporating motion correction into IVIM parameter fitting, and the self-supervised and end-to-end network design does not require training data, which can be extended to clinical applications.