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Abstract #1887

Feasibility Study of Fast Diffusion Tensor Imaging Based on Distributed Compressed Sensing

Yin Wu1, 2, Yan-Jie Zhu1, 2, Wei Liu1, 2, Qiu-Yang Tang1, 2, Yi-Shuo An1, 2, Ed X. Wu3, 4, Leslie Ying5, Dong Liang1, 2

1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 2Key Lab of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 3Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pukfulam, Hong Kong; 4Department of Electrical and Electronic Engineering, The University of Hong Kong, Pukfulam, Hong Kong; 5Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI, United States

DTI is a powerful tool to probe microstructure of biological tissues, but usually suffered from lengthy data acquisition. In the current study, theory of distributed compressed sensing (DCS) was applied to test its feasibility of accelerating DTI data sampling. Reconstruction performance was found to be related with SNR and reduction factor based on simulation study. Good reconstruction accuracy was numerically and visually achieved even at high accelerating rate of 4 for the experimental data. All the results indicate the feasibility of DCS to speed DTI data acquisition, which would greatly help to broaden its potential practical applications in the future.

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Keywords

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