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"Analysis of the Discretization Error vs. Estimation Time Tradeoff of MRF Dictionary Matching and the Advantage of the Neural Net-based Approach"
Chinmay Rao, Jakob Meineke, Nicola Pezzotti, Marius Staring, Matthias van Osch and Mariya Doneva
Abstract
Traditional MR fingerprinting involves matching the acquired signal evolutions against a dictionary of expected tissue fingerprints to obtain thecorresponding tissue parameters. Since this dictionary is essentially a discrete representation of a physical model and the matching processamounts to brute-force search in a discretized parameter space, there arises a tradeoff between discretization error and parameter estimationtime. In this work, we investigate this tradeoff and show via numerical simulation how a neural net-based approach solves it. We additionallyconduct 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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Copyright © 2023 by the authors.
Published version © 2023 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Rao:2023, |
| author |
= {Rao, Chinmay and Meineke, Jakob and Pezzotti, Nicola and Staring, Marius and van Osch, Matthias and Doneva, Mariya}, |
| title |
= {Analysis of the Discretization Error vs. Estimation Time Tradeoff of MRF Dictionary Matching and the Advantage of the Neural Net-based Approach}, |
| journal |
= {International Society for Magnetic Resonance in Medicine}, |
| month |
= {June}, |
| year |
= {2023}, |
| } |
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| last modified: 23-08-2023 |
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