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Deformable motion correction and spatial image analysis in positron emission tomography

University of British Columbia

Positron emission tomography (PET) is a molecular imaging modality that allows to quantitatively assess the physiological function of tissues in-vivo. Subject motion during imaging degrades the quantitative accuracy of the data. In small animal imaging, motion is minimized by the use of anesthesia, which interferes with the normal physiology of the brain. This can be circumvented by imaging awake rodents; however, in this case correction for non-cyclic motion with rigid and deformable components is required. In the first part of the thesis, the problem of motion correction in PET imaging of unrestrained awake rodents is addressed. A novel method of iterative image reconstruction is developed that incorporates correction for non-cyclic deformable motion. Point clouds were used to represent the imaged objects in the image space, and motion was accounted by using time-dependent point coordinates. The quantitative accuracy and noise characteristics of the proposed method were quantified and compared to traditional methods by reconstructing projection data from digital and physical phantoms. A digital phantom of a freely moving mouse was constructed, and the efficacy of motion correction was tested by reconstructing the simulated coincidence data from the phantom. In the second part of the thesis, novel approaches to PET image analysis were explored. In brain PET, analysis based on the tracer kinetic modeling (KM) may not always be possible due to the complexity of the scanning protocols and inability to find a suitable reference region. Therefore, the ability of KM-independent shape and texture metrics to convey useful information on the disease state was investigated, based on an ongoing Parkinson's disease study with radiotracers that probe the dopaminergic system. The pattern of the radiotracer distribution in the striatum was characterized by computing the metrics from multiple regions of interest defined using PET and MRI images. Regression analysis showed a significant correlation between the metrics and clinical disease measures (p<0.01). The effect of the region of interest definition and texture computation parameters on the correlation was investigated. Results demonstrate that there is clinically-relevant information in the tracer distribution pattern that can be captured using shape and texture descriptors.

Text

2017-01-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60277

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/