In this thesis, a methodology is developed to generate optimised three-dimensional voxel-based CT texture maps (3D-VTM) to examine regional heterogeneity information within tumours and their relation to tumour metabolism measured as 18F-fluoro-deoxy glucose (18F-FDG) Positron Emission Tomography (PET) distributions. Ten patients diagnosed with advanced non-small cell lung cancer (NSCLC) were investigated. For optimal texture information decoding, an optimised quantisation method is presented. The texture feature that reflects heterogeneity and which showed correlation with patients’ survival was chosen for this thesis. To account for respiratory motion effects, an in-house designed phantom was used to characterise the effects of motion on texture analysis and consequently adapt our method in that regard.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:703533 |
| Date | January 2017 |
| Creators | Alobaidli, Sheaka |
| Contributors | Evans, Phil |
| Publisher | University of Surrey |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://epubs.surrey.ac.uk/813156/ |
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