Abstract #2596

Non-Linear Optimization for Enhanced Parameter Retrieval in MR Fingerprinting

John T. Lundstrom^1,2, 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

Synopsis

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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