Fast image reconstruction with L2-regularization
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Fast image.pdf
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Author(s)
Bilgic, Berkin
Chatnuntawech, Itthi
Fan, Audrey P.
Setsompop, Kawin
Cauley, Stephen F.
Adalsteinsson, Elfar
Wald, Lawrence
Date Issued
November 2013
Journal
Journal of Magnetic Resonance Imaging
Publisher
Wiley Blackwell
Citation
Bilgic, Berkin, Itthi Chatnuntawech, Audrey P. Fan, Kawin Setsompop, Stephen F. Cauley, Lawrence L. Wald, and Elfar Adalsteinsson. “Fast Image Reconstruction with L2-Regularization.” Journal of Magnetic Resonance Imaging 40, no. 1 (November 4, 2013): 181–191.
Version
Author's final manuscript
Abstract
Purpose
We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction.
Materials and Methods
We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality.
Results
The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation.
Conclusion
For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1002/jmri.24365
