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dc.contributor.advisorSemple, Scotten
dc.contributor.advisorNewby, Daviden
dc.contributor.authorWang, Chengjiaen
dc.date.accessioned2017-07-19T10:01:51Z
dc.date.available2017-07-19T10:01:51Z
dc.date.issued2016-11-29
dc.identifier.urihttp://hdl.handle.net/1842/22918
dc.description.abstractDifferent medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.en
dc.contributor.sponsorMedical Research Council (MRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionCWang, KA Goatman and SIK Semple. (2016). Method of, and apparatus for, registration of medical images. US Patent: us 9275432.en
dc.relation.hasversionC Wang, KA Goatman, TJ MacGillivray, E Beveridge, Y Koutraki, J Boardman, C Stirrat, S Sparrow, E Moore, R Paraky, S Alam, MRDweck, C. Chin, C Gray, DE Newby, and SIK Semple. Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric k-means binning. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pages 1089–1092. IEEE, 2015.en
dc.relation.hasversionC Wang, YG Koutraki, O Mcbride, A Vesey, TJ MacGillivray, C Gray, DE Newby, KA Goatman, and SIK Semple. A robust automated multi-modality registration tool applied to abdominal aortic aneurysm. ISMRM-ESMRMB Joint Annual Meeting Proceedings, 2014, Milan, Italy.en
dc.relation.hasversionYG Koutraki, C Wang, J Robson, O Mcbride, RO Forsythe, TJ MacGillivray, C Gray, K, Goatman, J Camilleri-Brennan, DE Newby, and SIK Semple. Automatic detection of inflammatory ‘hotspots’ in abdominal aortic aneurysms to identify patients at risk of aneurysm expansion and rupture. ISMRM 23rd Annual Meeting Proceedings, 2015, Toronto, Canada.en
dc.relation.hasversionCWang, C Stirrat, S Alam, MR Dweck, C Chin, TJ Macgillivary, C Gray, R Pataky, and SIK Semple. Robust registration software of multi-parametric cardiac MR data with presence of motion-related artefacts and intensity non-homogeneity. ESMRMB 32nd Annual Scientific Meeting Proceedings, 2015, Edinburgh, UK.en
dc.relation.hasversionC Wang, T Macgillivary, YG Koutraki, JP Boardman, S Sparrow, E Moore, R Pataky, and SIK Semple. A robust automated multi-parametric registration software applied to neonatal MR neuro data. ESMRMB 32nd Annual Scientific Meeting Proceedings, 2015, Edinburgh, UK.en
dc.relation.hasversionYG Koutraki, O Mcbride, J Robson, RO Forsythe, C Wang, TJ MacGillivray, C Gray, K Goatman, J Camilleri-Brennan, J Jegadeeson, DE Newby, and SIK Semple. Automatic classification of abdominal aortic aneurysms to identify patients at risk of aneurysm expansion and rupture. ESMRMB 32nd Annual Scientific Meeting Proceedings, 2015, Edinburgh, UK.en
dc.relation.hasversionChengjia Wang, Georgia Koutraki, Olivia Mcbride, Alex Vesey, Tom MacGillivray, Calum Gray, David Newby, Keith Goatman, and Scott Semple. A robust automated multi-modality registration tool applied to abdominal aortic aneurysm [abstract]. In Joint Annual Meeting ISMRM-ESMRMB 2014, Milano, Italy, 2014.en
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectcomputed tomographyen
dc.subjectmagnetic resonance imagingen
dc.subjectimage registrationen
dc.subjectregistration software packagesen
dc.subjectmulti-modality dataen
dc.subjectparticle swarm optimizeren
dc.titleDevelopment of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imagingen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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