Keywords: AI Diffusion Models, IVIM
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.
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