GPU accelerated high-dimensional compressed sensing MRI

Recently, we have developed a tensor-decomposition based compressed sensing (CS) method for dynamic magnetic resonance imaging (dMRI) [1]. The proposed CS-dMRI method exploits the sparsity of the multi-dimensional MRI signal using Higher-order singular value decomposition (HOSVD). Our preliminary st...

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Bibliographic Details
Published in2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) pp. 648 - 651
Main Authors Zhen Feng, He Guo, Yinxin Wang, Yeyang Yu, Yang Yang, Feng Liu, Crozier, Stuart
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Compressed sensing
dynamic magnetic resonance imaging
GPU computing
Graphics processing units
Image reconstruction
Instruction sets
Magnetic resonance imaging
Tensile stress
Transforms
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Summary:Recently, we have developed a tensor-decomposition based compressed sensing (CS) method for dynamic magnetic resonance imaging (dMRI) [1]. The proposed CS-dMRI method exploits the sparsity of the multi-dimensional MRI signal using Higher-order singular value decomposition (HOSVD). Our preliminary study indicates that, compared with conventional approaches, the proposed CS method offers further acceleration in acquisition and also improves image quality. To further enhance the algorithm efficiency, in this work, we present a parallelized implementation of the HOSVD-based CS reconstructions using a graphics processing unit (GPU). The cine cardiac MRI study indicated the efficiency and accuracy of the GPU-accelerated high-dimensional CS-dMRI method.
DOI:10.1109/ICARCV.2014.7064380