Abstract
Respiratory motion degrades PET spatial resolution and image quality limiting the potential benefits from increased resolution. Motion correction is complicated by limitations of the poor statistical quality of PET data and there is still not a robust method available. Motion correction can be implemented at different stages of data processing either during or after reconstruction and may result in substantial improvements in image quality. The recent development of whole body PET-MRI scanners might provide a potential solution for motion correction since internal organ motion could be measured concurrently with PET using MRI. However, although there have been various proposed methods for motion correction, there is not sufficient evidence in the current literature to answer which method is better in clinical practice and investigating the impact of motion correction on lesion detectability.The aim of this thesis is to assess respiratory motion correction in terms of its quantitative accuracy and detectability performance to determine its potential for improved and early cancer diagnosis. This thesis is based on numerical 4D simulated PET data using real MRI data. Motion correction is investigated based on MRI-derived motion fields as could be obtained from a simultaneous PET-MRI acquisition. As a first step, this thesis aims to understand the behaviour of different approaches to motion-corrected image reconstruction in terms of convergence rate and the properties of the reconstructed images obtained. This thesis then deals with the impact of respiratory motion on lesion detectability. A comprehensive assessment is performed using different amplitudes for lesion displacement due to respiration and different respiration patterns derived from actual patient respiratory traces. The impact on the detectability is compared with that achievable by a higher resolution scanner in order to investigate the importance of correcting for motion to realise the benefit from the increased resolution of future PET scanners.
| Date of Award | 2014 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Paul Marsden (Supervisor) & Charalampos Tsoumpas (Supervisor) |