Therapeutic response assessment is a key component in adaptive image-guided radiotherapy. Conventional anatomic measures of response offer little information about the spatial distribution of tumor change. Recently developed voxel-wise response assessment methods operating on functional and biological imaging are better capable of evaluating the heterogeneity of response within the tumor, and thus may yield greater sensitivity than conventional approaches. However, voxel-wise analyses are limited by local registration uncertainties inherent to longitudinal imaging of tumors with changing morphology. A multi-resolution local histogram (LH) moment-based measure of voxel similarity was developed for the purpose of assessing the strength of correspondence between voxels of serial tumor images. This measure was first benchmarked through a series of experiments designed to establish robustness to image intensity variation and sensitivity to alterations in tissue structure through application of simulated deformations. The LH similarity method was subsequently developed as a means of mapping the spatial extent of structural change in tumors through the incorporation of an estimate of image complexity. The change maps were applied to a voxel-wise analysis of diffusion-weighted magnetic resonance imaging of patients with glioblastoma, acquired pre- and post-chemoradiotherapy. The sensitivity of the voxel-wise analysis in differentiating responding/stable patients from non-responding/progressing patients was improved by stratifying the analysis voxels according to regions of interest (ROI) based on the LH similarity-based estimate of tumor change. Meaningful correspondence relationships between evaluated voxels are essential for accurate image-based quantification of tumor structure and function with voxel-wise analysis techniques. The LH similarity methods developed here can robustly evaluate the quality of spatial and temporal voxel correspondence relationships and provide an automated tool for ROI selection and voxel change stratification. It is readily extendable to the analysis of the wide array of anatomic, functional and biological imaging currently used to characterize tumors, guide therapy and assess response.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/32735 |
| Date | 21 August 2012 |
| Creators | Hoisak, Jeremy |
| Contributors | Jaffray, David A. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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