Medical imaging plays an essential role in current clinical research and practice. Among the wealth of available imaging modalities, Positron Tomography Emission (PET) reveals functional processes in vivo by providing information on the interaction between a biological target and its tracer at the molecular level. A time series of PET images obtained from a dynamic scan depicts the spatio-temporal distribution of the PET tracer. Analysing the dynamic PET data then enables the quantification of the functional processes of interest for disease understanding and drug development. Given the time duration of a dynamic PET scan, which is usually 1-2 hours, any subject motion inevitably corrupts the tissue-tovoxel mapping during PET imaging, resulting in an unreliable analysis of the data for clinical decision making. Image registration has been applied to perform motion correction on misaligned dynamic PET frames, however, the current methods are solely based on spatial similarity. By ignoring the temporal changes due to PET tracer kinetics they can lead to inaccurate registration. In this thesis, a spatio-temporal registration framework of dynamic PET data is developed to overcome such limits. There are three scientific contributions made in this thesis. Firstly, the likelihood of dynamic PET data is formulated based on the generative model with both tracer kinetics and subject motion, providing a novel objective function. Secondly, the solution to the optimisation based on the generic plasma-input model is given, leading to the availability of a variety of biological targets. Thirdly, reference-input models are also incorporated to avoid blood sampling and thus extend the coverage of PET studies of the proposed framework. In the simulation-based validation, the proposed method achieves sub-voxel accuracy and its impact on clinical studies is evaluated on dopamine receptor data from an occupancy study, as well as breast cancer data from a reproducibility study. By successfully eliminating the motion artifacts as shown by visual inspection, the proposed method reduces the variability in clinical PET data and improves the confidence of deriving outcome measures on a study level. The motion correction algorithms developed in this thesis do not require any additional computational resources for a PET research centre, and they facilitate cost reduction by eliminating the need of acquiring extra PET scans in cases of motion corruption.

Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

CAPES / Tratamentos de radiocirurgia requerem elevada precisão, pois envolvem lesões de pequenas dimensões, as quais são tratadas em poucas frações de altas doses de radiação. Sendo assim, a localização precisa da região de interesse é de grande importância para o sucesso do tratamento radioterápico. Neste trabalho foi investigada a interferência da caixa localizadora do alvo em tais procedimentos, utilizando imagens tomográficas do crânio de um phantom antropomórfico para simular um tratamento de radiocirurgia utilizando duas técnicas de exposição conforme protocolo do Hospital Israelita Albert Einstein. Todo o processo de planejamento radioterápico de um tratamento de radiocirurgia foi executado, incluindo o cálculo da dose média de radiação com e sem diferenças de heterogeneidades para todas as exposições realizadas, utilizando os algoritmos de cálculo de dose AAA e PBC. Foi realizada também uma análise da variância dos valores dos pixels nos histogramas dos números CT para analisar a interferência do uso da caixa de localização na qualidade das imagens tomográficas adquiridas. Os experimentos realizados indicam que o algoritmo AAA é menos susceptível a diferenças no cálculo dos valores das doses médias. Quanto ao ruído, os experimentos realizados com a caixa localizadora do alvo demostraram maior perda na qualidade da imagem tomográfica. / Radiosurgery treatments require high precision because they involve lesions of small dimensions, which are treated with elevated radiation doses in a few fractionated sessions. Thus, the localization accuracy of the region of interest is very important for successful radiation therapy. In this study, the interference of the target localizer box in such procedures was investigated using tomographic images of the skull of an anthropomorphic phantom in order to simulate a radiosurgery treatment using two exposure techniques according to a protocol of the Albert Einstein Hospital. The radiosurgery treatment process was performed, including the calculation of the average radiation dose with and without tissue inhomogeneity considerations for all exposures, using the AAA and PBC dose calculation algorithms. An analysis of the variance of pixel values in the CT number histogram was also conducted in order to analyze the interference of the target localization box on the quality of the acquired tomographic images. The experiments indicate that the AAA algorithm is less susceptible to differences in the calculation of the average doses. Regarding noise, the experiments performed with the target localization box demonstrated greater loss in tomographic image quality.