Accelerated MR diffusion tensor imaging using distributed compressed sensing
Magnetic Resonance in Medicine, 2014•Wiley Online Library
Purpose Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the
orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data
acquisition is highly desirable. In this article, the feasibility and efficacy of distributed
compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in
diffusion‐weighted images. Methods Fully sampled DTI datasets were obtained from both
simulated phantom and experimental heart sample, with diffusion gradient applied in six …
orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data
acquisition is highly desirable. In this article, the feasibility and efficacy of distributed
compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in
diffusion‐weighted images. Methods Fully sampled DTI datasets were obtained from both
simulated phantom and experimental heart sample, with diffusion gradient applied in six …
Purpose
Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion‐weighted images.
Methods
Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k‐space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion‐weighted images were reconstructed by solving an l2‐l1 norm minimization problem. Reconstruction performance with varied signal‐to‐noise ratio and acceleration factors were evaluated by root‐mean‐square error and maps of reconstructed DTI indices.
Results
Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal‐to‐noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4.
Conclusion
Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically. Magn Reson Med 71:763–772, 2014. © 2013 Wiley Periodicals, Inc.
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