Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF
Bhairav Bipin Mehta
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorDan Ma
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorEric Yann Pierre
Imaging Division, The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
Search for more papers by this authorYun Jiang
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorSimone Coppo
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorCorresponding Author
Mark Alan Griswold
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
Correspondence Mark Alan Griswold, Case Western Reserve University, 11100 Euclid Avenue - Bolwell B121, Cleveland, OH 44106, USA. Email: [email protected]Search for more papers by this authorBhairav Bipin Mehta
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorDan Ma
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorEric Yann Pierre
Imaging Division, The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
Search for more papers by this authorYun Jiang
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorSimone Coppo
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Search for more papers by this authorCorresponding Author
Mark Alan Griswold
Department of Radiology, Case Western Reserve University, Cleveland, Ohio
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
Correspondence Mark Alan Griswold, Case Western Reserve University, 11100 Euclid Avenue - Bolwell B121, Cleveland, OH 44106, USA. Email: [email protected]Search for more papers by this authorFunding Information: Support for this study was provided by NIH 1R01EB016728-01A1, NIH 5R01EB017219-02, and Siemens Healthcare. This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University
Abstract
Purpose
The purpose of this study is to increase the robustness of MR fingerprinting (MRF) toward subject motion.
Methods
A novel reconstruction algorithm, MOtion insensitive MRF (MORF), was developed, which uses an iterative reconstruction based retrospective motion correction approach. Each iteration loops through the following steps: pattern recognition, metric based identification of motion corrupted frames, registration based motion estimation, and motion compensated data consistency verification. The proposed algorithm was validated using in vivo 2D brain MRF data with retrospective in-plane motion introduced at different stages of the acquisition. The validation was performed using qualitative and quantitative comparisons between results from MORF, the iterative multi-scale (IMS) algorithm, and with the IMS results using data without motion for a ground truth comparison. Additionally, the MORF algorithm was evaluated in prospectively motion corrupted in vivo 2D brain MRF datasets.
Results
For datasets corrupted by in-plane motion both prospectively and retrospectively, MORF noticeably reduced motion artifacts compared with iterative multi-scale and closely resembled the results from data without motion, even when ∼54% of data was motion corrupted during different parts of the acquisition.
Conclusions
MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.
CONFLICT OF INTEREST
The authors receive research support from Siemens.
Supporting Information
Additional Supporting Information may be found in the supporting information tab for this article.
| Filename | Description |
|---|---|
| mrm27227-sup-0001-suppinfo01.docx4.1 MB |
FIGURE S1 Origin of the components used for identification of motion corrupted frames FIGURE S2 Example metric curves illustrating usefulness of NMI with T2 map metric FIGURE S3 Example metric curves illustrating usefulness of NMI with T1 map metric FIGURE S4 Example metric curves illustrating usefulness of RMSE metric FIGURE S5 Comparison of fully sampled data with undersampled data and direct matching with IMS algorithm. We see a few differences between results using direct matching from fully sampled data (left) and results using direct matching from undersampled data (center). However, results using direct matching and IMS algorithm (right) from undersampled data are in close agreement |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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