Abstract #1840

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

Zhongbiao Xu¹, Li Guo¹, Wenxing Fang², Chenguang Zhao², Yingjie Mei^1,3, Zhifeng Chen⁴, Wufan Chen¹, Ed X. Wu^5,6, 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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