Deep learning improves breast IVIM estimation in better benign and malignant lesion differentiation

Intravoxel incoherent motion (IVIM) with multiple b-values, as an advanced diffusion model, provides accurate identification of breast cancer. However, the IVIM-derived parameters vary greatly depending on different fitting methods, especially for parameters D* and f. In this study, we proposed a method for high-quality breast IVIM reconstruction based on deep neural network. Data analysis shows that our proposed method improves the visual quality of breast IVIM parametric maps with better benign and malignant breast lesion differentiation ability compared to the traditional least-square fitting method.