Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling: A Subspace Approach to Improved MRF Reconstruction
Name
nihms860285.pdf
Description
Accepted version
Size
1.86 MB
Format
Adobe PDF
Checksum (MD5)
73cda0099f59ffe363d7b6217dfd616e
Author(s)
Zhao, Bo
Setsompop, Kawin
Adalsteinsson, Elfar
Gagoski, Borjan
Ye, Huihui
Ma, Dan
Jiang, Yun
Ellen Grant, P
Griswold, Mark A
Wald, Lawrence L
Date Issued
2018
Journal
Magnetic Resonance in Medicine
Publisher
Wiley
Citation
Zhao, B., et al. "Improved Magnetic Resonance Fingerprinting Reconstruction with Low-Rank and Subspace Modeling." Magn Reson Med (2017).
Version
Author's final manuscript
Abstract
© 2017 International Society for Magnetic Resonance in Medicine Purpose: This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). Theory and Methods: A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T1, T2, and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. Results: The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. Conclusions: The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933–942, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1002/MRM.26701
https://doi.org/10.1002/MRM.26701
