Non-Linear Optimization for Enhanced Parameter Retrieval in MR Fingerprinting
John T. Lundstrom1,2, Megan E. Poorman3, Andrew Dienstfrey4, and Kathryn E. Keenan3
1Department of Physics, University of Colorado, Boulder, CO, United States, 2Associate of the National Institute of Standards and Technology, Boulder, CO, United States, 3Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, United States, 4Information Technology Laboratory, National Institute of Standards and Technology, Boulder, CO, United States
We investigate non-linear optimization to produce quantitative parameter maps in MR Fingerprinting. Our Monte Carlo analysis shows non-linear optimization to be robust with respect to noise. We also find non-linear optimization to yield consistent results when initialized by dictionary matching using either sparse or densely populated dictionaries. Our research outcomes suggest non-linear optimization is an effective enhancement to the MR Fingerprinting pipeline, allowing for smaller dictionary sizes and more accurate parameter retrieval. Future work will include enhancements to, and analysis of, the computational efficiency of non-linear optimization.
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