Abstract #1863

A Study on Total-Variation Regularization for Model-Based Reconstruction in DTI

Kazem Hashemizadeh¹, Samer Merchant², Dong Liang³, Rong-Rong Chen¹, Edward Dibella^1,2,4, Edward Hsu², and Leslie Ying⁵

¹Dept. of Electrical and Computer Engineering, University of Utah, SALT LAKE CITY, UT, United States, ²Department of Biomedical Engineering, University of Utah, SALT LAKE CITY, UT, United States, ³Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China, ⁴Department of Radiology and Imaging Sciences, University of Utah, SALT LAKE CITY, UT, United States, ⁵Department of Biomedical Engineering, The State University of New York (SUNY) at Buffalo, NY, United States

In this work, we study total-variation (TV) regularization for model-based reconstruction from undersampled DTI data. Various TV regularization methods are examined. Using ex-vivo brain DTI data, we show that imposing TV constraints on DWI provide more reliable quantitative estimates of diffusion than those imposing TV constraints directly on the tensor. A gradient descent algorithm with line backtracking is used for better convergence to optimal solution. For highly undersampled data of 12 diffusion encoding directions and a reduction factor of R=4, we show that good estimates of primary eigen-vector, fractional anisotropy, and mean diffusivity can still be obtained using TV-based regularization.

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