Papp, Daniel;
(2019)
Robust and Fast Quantitative MRI for Clinical Deployment.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Within this thesis, my work carried out in order to prepare an existing quantitative imaging method, multi-parameter mapping, for clinical use, is summarized. My tasks were to improve the motion-robustness of the acquisitions used in this protocol, and to reduce the scan time of the protocol to a clinically viable level. In order to reduce acquisition times, I investigated the use of higher parallel imaging acceleration factors, compared to those used in the protocol to date. I found that increasing the acceleration factor from 2 to 2-by-2 is a viable approach to decrease scan time, as is elliptical k-space coverage. In order to improve the robustness versus inter-scan motion, I investigated the effect of inter-scan motion on the quantitative maps derived from the protocol. I found that, while rigid-body motion correction is not sufficient in cases where a map is calculated from more than one scan, as the changes in the receive field are unaccounted for. I introduced a correction method, based on measuring the receive field for each structural scan, and showed that it improves image quality in the presence of inter-scan motion. Motion robustness was also improved by selecting a relatively motioninsensitive acquisition trajectory, from a set of clinically available trajectories. To further address the issue of intra-scan motion, I developed a novel navigator technique, based on acquiring data concurrent with gradient spoiling. Crucially, the acquisition of this navigator did not require additional scan time. I found that this navigator is sufficiently sensitive to motion, such that outlier rejection can be used to identify motion-corrupted data points. I implemented a data re-acquisition approach, based on the outlier rejection, and showed that image quality can be improved by this method.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Robust and Fast Quantitative MRI for Clinical Deployment |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. |
| UCL classification: | UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068912 |
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