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Abstract #1840

Model-Based Joint Reconstruction for Multi b-Value Diffusion-Weighted Imaging

Zhongbiao Xu1, Li Guo1, Wenxing Fang2, Chenguang Zhao2, Yingjie Mei1,3, Zhifeng Chen4, Wufan Chen1, Ed X. Wu5,6, Feng Huang7, and Yanqiu Feng1

1School of Biomedical Engineering, Guangdong Provincial Key Laborary of Medical Image Processing, Southern Medical University, Guangzhou, People's Republic of China, 2Philips Healthcare (Suzhou), Suzhou, People's Republic of China, 3Philips Healthcare, Guangzhou, People's Republic of China, 4Department of Biomedical Engineering, Zhejiang University, Hangzhou, People's Republic of China, 5laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China, 6Department of Electrical and Electronic Engineering,The University of Hong Kong, Hong Kong SAR, People's Republic of China, 7Neusoft Medical System, Shanghai, People's Republic of China

In current multi b-value DWI, each b-value image is usually reconstructed independently by using parallel MRI techniques. In this work, we propose a model-based joint reconstruction method for the reconstruction of under-sampled multi b-value DWI data. The proposed method can directly estimate quantitative parameters form k-space data, and exploit inter-image constraint to improve the quality of reconstructed image.

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