Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data. The compressed sensing algorithm models image reconstruction as a linear combination minimization problem that includes data fidelity terms, sparse priors, and total variation terms. Sparse representation is a key assumption of the compressed sensing theory, and the quality of reconstruction largely depends on sparse transformation. In this article, we proposed a compressed sensing low-field MRI reconstruction algorithm that combined dual-tree wavelet transform and wavelet tree sparsity. Experimental results demonstrated that the proposed algorithm had certain advantages over the conventional reconstruction algorithm, in terms of certain objective evaluation indicators.
CHAI Qing-huan
,
SU Guan-qun
,
NIE Sheng-dong
. Compressive Sensing Low-Field MRI Reconstruction with Dual-Tree Wavelet Transform and Wavelet Tree Sparsity[J]. Chinese Journal of Magnetic Resonance, 2018
, 35(4)
: 486
-497
.
DOI: 10.11938/cjmr20182645
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