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Abstract #0196

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

Shuhao Shi1, Lu Wang1, Jianfeng Bao2, Zhigang Wu3, Congbo Cai1, Zhong Chen1, Jiazheng Wang3, and Shuhui Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 3MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Synopsis

Keywords: Breast, CancerIntravoxel 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.

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