Fast TVL1-L2 MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart

Fast TVL1-L2 MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart
Title:
Fast TVL1-L2 MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart
Other Titles:
2015 IEEE International Conference on Digital Signal Processing (DSP)
DOI:
10.1109/ICDSP.2015.7252046
Keywords:
Publication Date:
21 July 2015
Citation:
S. Xie, C. Guan, W. Huang and Z. Lu, "Fast TVL1-L2 MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart," 2015 IEEE International Conference on Digital Signal Processing (DSP), Singapore, 2015, pp. 1085-1088. doi: 10.1109/ICDSP.2015.7252046
Abstract:
This paper presents a fast algorithm for magnetic resonance (MR) image reconstruction from undersampled k-space measurements. The underlying MR image reconstruction is formulated as solving a TVL1-L2 minimization problem whose objective function consists of total variation (TV) regularizer, wavelet-based l1-norm regularizer and l2 data fidelity. Our approach is based on a variable splitting strategy and an accelerated alternating direction method of multiplier (ADMM) with restart. This paper shows that our proposed algorithm is fast and efficient for solving the TVL1-L2 MR image reconstruction problem. More precisely, a variable splitting method is used to split the variable into three variables and obtain an equivalent constrained optimization formulation, which is then addressed with an accelerated ADMM with adaptive restart. This ADMM algorithm is acceleration because the next iterate is computed by employing two previous computed iterates, and the restart rule is employed to enforce monotonicity and convergence in solving weakly convex TVL1-L2 optimization. Moreover thanks to intrinsic spatial-frequency encoding in MRI data, the inverse of regularized Hessian matrix can perform efficiently by exploiting fast Fourier transform (FFT) and fast wavelet transform (or tight frame). Experimental examples also demonstrate that the proposed algorithm is fast and efficient compared to the classical ADMM in TVL1-L2 MR image reconstruction.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1546-1874
2165-3577
Files uploaded:

File Size Format Action
dsp2015-p0205-xie.pdf 211.99 KB PDF Open