Improved magnetic resonance fingerprinting reconstruction with low‐rank and subspace modeling

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 tiss...

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Bibliographic Details
Published in:Magnetic resonance in medicine 2018-02, Vol.79 (2), p.933-942
Main Authors: Zhao, Bo, Setsompop, Kawin, Adalsteinsson, Elfar, Gagoski, Borjan, Ye, Huihui, Ma, Dan, Jiang, Yun, Ellen Grant, P., Griswold, Mark A., Wald, Lawrence L.
Format: Article
Language:English
Subjects:
Aliasing
Contamination
Data acquisition
Fingerprinting
Image processing
Image quality
Image reconstruction
In vivo methods and tests
low‐rank modeling
Magnetic resonance
Mathematical models
Model accuracy
model‐based imaging
MR fingerprinting
Noise pollution
Parameter estimation
Pattern matching
quantitative MRI
relaxometry
Resonance
subspace modeling
Subspaces
Time series
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Summary: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.
ISSN:0740-3194
1522-2594
DOI:10.1002/mrm.26701