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Title: Maximising the diagnostic value of structural MRI in the diagnosis of dementia : a comprehensive study of post-mortem proven cases
Author: Harper, L.
ISNI:       0000 0004 7428 9409
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2015
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This thesis investigates the use of atrophy patterns from structural brain imaging to distinguish different dementia pathologies, including Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia pathologies. Using gold standard histopathology to stratify groups, analysis is based on 3D-T1-weighted imaging acquired during life in patients who attended clinic in one of three European centres. As well as comparison of disease groups with healthy controls, more clinically relevant comparisons between disease groups are performed to identify features that may be useful for differential diagnosis. The image analysis techniques used in this thesis range from simple visual assessment to more advanced machine learning. Visual rating scales were found to be reliable, quick to perform, and when used in combination, could achieve diagnostic accuracy equal to unstructured visual assessment by dementia experts. Voxel based morphometry, used to provide a comprehensive estimate of global patterns of atrophy in pathologically distinct dementias, confirmed findings in the literature based on clinical data, and identified novel regions of interest for further study. A fully automated diagnostic approach using multi-atlas segmentation propagation and support vector classifiers, revealed brain volume differences between pathologically distinct groups, yet with several technical limitations to address. Since histopathological diagnosis is rare in such a large, pathologically diverse cohort, this thesis also considers opportunities to develop the dataset into a shared resource for the dementia research community. To this end, a web application was developed to allow the data to be shared between collaborating centres, with plans to adapt this into a teaching resource. In summary, this thesis uses a variety of analysis techniques to identify imaging features that may be useful for the differential diagnosis of dementia pathologies. Various opportunities are explored to maximise the value that can be derived from this unique and valuable dataset.
Supervisor: Schott, J. M. ; Fox, N. C. ; Ridgway, G. R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available