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Title: Robust and fast quantitative MRI for clinical deployment
Author: Papp, Daniel
ISNI:       0000 0004 7661 1019
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available