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Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling: A Subspace Approach to Improved MRF Reconstruction

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; ... Show more Show less
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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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.
Date issued
2018
URI
https://hdl.handle.net/1721.1/135755
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
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

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