Paper Info
Title
Fast reconstruction for accelerated multi-slice multi-contrast MRI
Abstract
Clinical magnetic resonance imaging (MRI) protocols typically include multiple acquisitions of the same region of interest under different contrast settings. This paper presents an efficient algorithm to jointly reconstruct a set of undersampled images with different contrasts. The proposed method has faster reconstruction time and better quality as measured by the normalized root-mean-square error (RMSE) compared to the existing methods consisting of multi-contrast Fast Composite Splitting Algorithm (FCSA-MT), Multiple measurement vectors FOCal Underdetermined System Solver (M-FOCUSS), and total variation regularized compressed sensing (SparseMRI). To efficiently solve the £2, 1-regularized optimization problem, our proposed algorithm adopts the Split Bregman (SB) technique to divide the problem into sub-problems. We efficiently compute a closed-form solution to each of the sub-problems by implementing a 3D spatial gradient operator as element-wise multiplication in k-space. As demonstrated by the in vivo results, the proposed algorithm (SB-L21) offers 2x, 32x, and 66x faster reconstruction with lower RMSE averaged across all contrasts and slices compared to FCSA-MT, M-FOCUSS, and SparseMRI, respectively.
Year
DOI
Venue
2015
10.1109/ISBI.2015.7163881
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Keywords
Field
DocType
Magnetic resonance imaging (MRI),compressed sensing,multi-contrast imaging
Iterative reconstruction,Computer vision,Underdetermined system,Pattern recognition,Computer science,Mean squared error,Artificial intelligence,Solver,Region of interest,Real-time MRI,Optimization problem,Compressed sensing
Conference
ISSN
Citations 
PageRank 
1945-7928
0
0.34
References 
Authors
5
5
Name
Order
Citations
PageRank
Itthi Chatnuntawech1162.05
Berkin Bilgic29510.43
Adrián Martín362.53
Kawin Setsompop423820.09
Elfar Adalsteinsson512916.18