Abstract #3419
Highly Undersampling MR Image Reconstruction Using Tree-Structured Wavelet Sparsity and Total Generalized Variation Regularization
Ryan Wen Liu 1 , Lin Shi 2 , Simon C.H. Yu 1 , and Defeng Wang 1,3
1
Department of Imaging and Interventional
Radiology, The Chinese University of Hong Kong, Shatin,
N.T., Hong Kong,
2
Department
of Medicine and Therapeutics, The Chinese University of
Hong Kong, Shatin, N.T., Hong Kong,
3
Department
of Biomedical Engineering and Shun Hing Institute of
Advanced Engineering, The Chinese University of Hong
Kong, Shatin, N.T., Hong Kong
In this study, we propose to combine
L
0
regularized
tree-structured wavelet sparsity (TsWS) and second-order
total generalized variation (TGV
2
) to
reconstruct MR image from highly undersampled k-space
data. In particular, the
L
0
regularized
TsWS could better represent the measure of sparseness in
wavelet domain. TGV
2
is
capable of maintaining trade-offs between artefact
suppression and tissue feature preservation. To achieve
solution stability, the corresponding minimization
problem is decomposed into several simpler subproblems.
Each of these subproblems has a closed-form solution or
can be efficiently solved using existing optimization
algorithms. Experimental results have demonstrated the
superior performance of our proposed method.
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