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Abstract #2212

Analysis of the Discretization Error vs. Estimation Time Tradeoff of MRF Dictionary Matching and the Advantage of the Neural Net-based Approach

Chinmay Rao1, Jakob Meineke2, Nicola Pezzotti3, Marius Staring1, Matthias van Osch1, and Mariya Doneva2
1Leiden University Medical Center, Leiden, Netherlands, 2Philips Research, Hamburg, Germany, 3Philips Research, Eindhoven, Netherlands

Synopsis

Keywords: MR Fingerprinting/Synthetic MR, MR FingerprintingTraditional MR fingerprinting involves matching the acquired signal evolutions against a dictionary of expected tissue fingerprints to obtain the corresponding tissue parameters. Since this dictionary is essentially a discrete representation of a physical model and the matching process amounts to brute-force search in a discretized parameter space, there arises a tradeoff between discretization error and parameter estimation time. In this work, we investigate this tradeoff and show via numerical simulation how a neural net-based approach solves it. We additionally conduct a phantom study using 1.5T and 3T data to demonstrate the consistency of neural net-based estimation with dictionary matching.

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Keywords