Abstract：
:Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The joint reconstruction problem is formulated as minimizing a linear combination of three terms, corresponding to a least squares data fitting, joint total variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this problem, we decompose it into joint TV regularization and group sparsity subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework. Experiments demonstrate the efficiency and effectiveness of the proposed method compared to existing multi-contrast MRI methods.
journal_name
Magn Reson Imagingjournal_title
Magnetic resonance imagingauthors
Huang J,Chen C,Axel Ldoi
10.1016/j.mri.2014.08.025subject
Has Abstractpub_date
2014-12-01 00:00:00pages
1344-52issue
10eissn
0730-725Xissn
1873-5894pii
S0730-725X(14)00256-2journal_volume
32pub_type
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