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Abstract #1146

IVIM reconstruction from highly under-sampled DW-PROPELLER acquisition data via synthetic data-driven physics-informed deep learning

Jiechao Wang1, Wenhua Geng1, Jian Wu1, Taishan Kang2, Zhigang Wu3, Jianzhong Lin2, Congbo Cai1, and Shuhui Cai1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, 2Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, 3Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Synopsis

Keywords: Quantitative Imaging, Diffusion/other diffusion imaging techniquesA synthetic data-driven physics-informed network (SDDPI-Net) was proposed for intravoxel incoherent motion (IVIM) mapping based on highly under-sampled diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) data. This reconstruction network directly estimated distortion-free and artifacts-free IVIM parameters by explored data redundancy in the k-b space and IVIM bi-exponential model with synthetic training data. The results of human brain experiments show that our method can significantly improve the accuracy of IVIM maps with 6´ under-sampled DW-TSE-PROPELLER than other methods.

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Keywords