A Monte Carlo model has been developed using MCNP5 to simulate the Nucletron Ir-192 HDR source in order to investigate the influence of tissue heterogeneities on dose calculations compared to the dose in homogeneous water media, as it is typically calculated by brachytherapy Treatment Planning Systems (TPS). Validity of the simulation was verified in water medium in comparison with peer reviewed results using the dosimetric parameters recommended by AAPM, Task Group-43. The dose-rates in simulated prostate, bladder and rectum were compared to those obtained in the homogeneous water phantom. Based on the resulting dose differences, it is inferred that TPS algorithms for brachytherapy dose calculations overestimate the dose to tissues like prostate and bladder by up to 49%. A clinically relevant dose underestimation of 5.5% to the rectum was also found. We recommend that further investigation using actual patient CT data as input to the Monte Carlo simulation be performed. / by Ramsâes Herrera. / Thesis (M.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

In this research three innovative registration systems were designed with the configurations of the mutual information and optimization technique: (1) mutual information combined with the downhill simplex method of optimization. (2) the derivative of mutual information combined with Quasi-Newton method. (3) mutual information combined with hybrid genetic algorithm (large-space random search) to avoid local maximum during the optimization. These automatic registration systems were evaluated with a variety of images, dimensions and voxel resolutions. Experiments demonstrate that registration system combined with mutual information and hybrid genetic algorithm can provide robust and accurate alignments to obtain a composite activation map for functional MRI analysis.

Technological advances in image acquisition speeds and new contrast agents, in both clinical and basic research settings, have enabled entirely new approaches to functional imaging in living systems. Analysis of dynamic and multidimensional data requires very different approaches to the classical segmentation and visualization tools developed for purely structural or anatomical imaging. This thesis details the development of two different spatiotemporal analysis approaches for high-speed in-vivo dynamic optical imaging. Optical imaging is a diverse, versatile, and generally inexpensive modality that can take advantage of a wide range of endogenous and exogenous sources of optical contrast within living tissue. While light scattering can limit resolution and sensitivity of imaging in deeper tissues, optical imaging is well suited for small animal studies where it can be used for studies of physiology and disease processes, for pharmaceutical development and as a test-bed for translation to clinical applications. In the first part of this work, we present and apply spatiotemporal analysis techniques which we define as `dynamic contrast enhancement' methods. We apply these methods to in-vivo whole body small animal molecular optical imaging to demonstrate that dynamic analysis can be used for longitudinal assessment of organ function. We then demonstrate the equivalence of our approach to dynamic contrast enhanced magnetic resonance imaging. This optical technique could allow for better informed drug development and longitudinal toxicity evaluation. This technique could also serve as a platform for the development of functional imaging methods using dynamic MRI. We then apply spatiotemporal analysis techniques to high speed optical hemodynamic imaging data acquired on the exposed rodent cortex. The purpose of this work is to develop a mechanistically-based spatiotemporal model of neurovascular coupling, in order to better understand the basis of functional magnetic resonance imaging data in the human brain. Our results also provide new insights into potential links between neurovascular disruption and disease pathophysiology in the brain.

Advances in medical imaging technology have led to the acquisition of large number of images in different modalities. On some of these images the boundaries of key organs need to be accurately identified for treatment planning and diagnosis. This is typically performed manually by a physician who uses prior knowledge of organ shapes and locations to demarcate the boundaries of organs. Such manual segmentation is subjective, time consuming and prone to inconsistency. Automating this task has been found to be very challenging due to poor tissue contrast and ill-defined organ/tissue boundaries. This dissertation presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative location of objects into a single framework in order to perform automated segmentation. The algorithm has been tested on two different datasets: for segmenting hands on thermographic images and for prostate segmentation on pelvic computed tomography (CT) and magnetic resonance (MR) images. In this dissertation we report the results of segmentation in two dimensions (2D) for thermographic images; and two as well as three dimensions (3D) for pelvic images. We show that combining multiple features for segmentation improves segmentation accuracy as compared with segmentation using single features such as texture or shape alone.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 109-112). Also available on the Internet.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: digital x-ray imaging; pixel size; scintillator. Includes bibliographical references (p. 57-59).

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 109-112). Also available on the Internet.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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