Sequence optimisation for Magnetic Resonance Fingerprinting

Title

Sequence optimisation for Magnetic Resonance Fingerprinting

 Author

Heesterbeek, David (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Applied Sciences; TU Delft ImPhys/Medical Imaging; TU Delft Numerical Analysis)

Contributor

Vos, F.M. (mentor) ORCID 0000-0003-2996-6872
van Gijzen, M.B. (mentor) ORCID 0000-0002-6491-9564
Weingärtner, S.D. (graduation committee) ORCID 0000-0002-0739-6306
Verschuur, D.J. (graduation committee)
van Gennip, Y. (graduation committee) ORCID 0000-0003-4953-8314
Nagtegaal, M.A. (mentor) ORCID 0000-0002-6563-865X

 Degree granting institution

Delft University of Technology

 Date

2021-09-27

 Abstract

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach for simultaneously estimating multiple quantitative maps in one acquisition. Sequence optimisation for MRF can be a powerful tool in increasing the accuracy an precision of the quantitative results. Multi-component analysis in the MRF framework can distinguish multiple different tissues in one voxel such as myelin water and white matter which play an important role in monitoring progressive diseases such as multiple sclerosis. Using the estimation theoretic Cramér-Rao bound, optimisations of the acquisition sequences can be performed, that increase the precision of the resulting tissue maps. The effect of this optimisation has been confirmed using numerical simulations. Speed-ups in MRF are generated using significant undersampling of the k-space information. This results in spatially coherent undersampling artefacts, that generally is the dominating error source for regular $T_1$ and $T_2$ mapping. The undersampling artefacts can be predicted using a mathematical model leveraging on techniques from perturbation theory. Numerical simulations suggested that optimisations of the acquisition parameters are effective in reducing the undersampling error. This was confirmed using in vivo scans. The optimisations resulting from these two different models are easily implemented in future clinical practice.

 Subject

Magnetic Resonance Imaging
Magnetic Resonance Fingerprinting
Numerical Optimisation

 To reference this document use:

http://resolver.tudelft.nl/uuid:5d4da8c5-7d2d-4861-8b6f-6205125197b8

 Embargo date

2022-03-01

 Part of collection

Student theses

 Document type

master thesis

 Rights

© 2021 David Heesterbeek
Files
PDF MEP_D.G.J._Heesterbeek.pdf 13.12 MB
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