Deep Learning based Tissue Specific MRI-IVIM Parameter Estimation

Motivation: IVIM imaging is essential for distinguishing breast tumor subtypes, but traditional methods are noise-sensitive, and current deep learning approaches are limited by tissue heterogeneity. Goal(s): To propose a tissue-specific deep learning approach to optimize estimation performance compared to single-model estimation approach. Approach: Two stages: tissue segmentation and tissue-specific parameter estimation. U-Net model segments tissue types from DWI data. Next, MLPs are trained with masks for each tissuetype to estimate IVIM parameters, which are then combined for the final estimation. Results: With the segmentation result, the estimation results show better performance, and the distribution more closely aligned with the ground truth, particularly for tumors. Impact: IVIM model has gained momentum recently, especially in the field oncology. Our study improve the parameter estimation performance for IVIM model, which is important for tumor and tumor type prediction.