Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling
Name
nihms955829.pdf
Description
Accepted version
Size
1001.59 KB
Format
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Checksum (MD5)
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Author(s)
Zhao, Bo
Bilgic, Berkin
Adalsteinsson, Elfar
Griswold, Mark A.
Wald, Lawrence L.
Setsompop, Kawin
Date Issued
September 2017
Journal
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhao, Bo et al. "Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling." 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2017, Seogwipo, South Korea, Institute of Electrical and Electronics Engineers, September 2017 © 2017 IEEE
Version
Author's final manuscript
Abstract
Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/embc.2017.8037553
