Fast multi-contrast MRI reconstruction

Authors:
Junzhou Huang;Chen Chen;Leon Axel
Affiliations:
Department of Computer Science and Engineering, University of Texas at Arlington, TX;Department of Computer Science and Engineering, University of Texas at Arlington, TX;Department of Radiology, New York University, New York, NY
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

Citing 6
Cited 0

Multitask compressive sensing

IEEE Transactions on Signal Processing
Probing the Pareto Frontier for Basis Pursuit Solutions

SIAM Journal on Scientific Computing
Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems

IEEE Transactions on Image Processing
Efficient MR image reconstruction for compressed MR imaging

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Compressed sensing

IEEE Transactions on Information Theory
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an efficient algorithm to simultaneously reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The simultaneous reconstruction problem is formulated as minimizing a linear combination of three terms corresponding to a least square 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 formulation, we decompose it into group sparsity and joint TV regularization subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods on SRT24 multi-channel Brain Atlas Data. Experiments demonstrate its superior performance for multi-contrast MR image reconstruction.