title: Fast multi-contrast MRI reconstruction.
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.
authors.affiliation: Department of Computer Science, Engineering, University of Texas at Arlington, 500 UTA Boulevard, Arlington, TX 76019, United States. Electronic address: jzhuang@uta.edu.
authors.forename: Junzhou
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Computer Science, Engineering, University of Texas at Arlington, 500 UTA Boulevard, Arlington, TX 76019, United States.
authors.forename: Chen
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Radiology, New York University, New York, NY 10016, United States.
authors.forename: Leon
authors.initials: L
authors.lastname: Axel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D019245:Compressive Strength; D003287:Contrast Media; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007596:Joints / Q000473:pathology*; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016011:Normal Distribution; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012984:Software
chemical_list: D003287:Contrast Media
keywords: Compressed sensing MRI; Joint sparsity; Joint total variation; Multi-contrast MRI