A complete scientific understanding of human nature requires delineation of the neurobiological characteristics underlying the unique features of the human mind. This effort can be facilitated by comparing the human brain with the brains of other living primate species. Humans are more susceptible to neurodegenerative diseases than other primate species, including our closest living primate relatives, the chimpanzees. Comparing age-related changes in brain structure between humans and non-human primates could, therefore, potentially shed light on the neurological basis of this human vulnerability. Further, human brains are lateralized with specialized cognitive and behavioral functions. Comparing the magnitude of hemispheric asymmetries in brain structure between humans and non-human primates can probe insights into this human specific capability and learn more about human evolution. Diffusion weighted MRI protocols were developed for different species, taking into account their neuroanatomical differences. For Chimpanzees, a multi-shot DWI sequence was developed and compared with a single-shot DWI sequence to determine which provided a better quality diffusion data free of acquisition related artifacts. Different simulation techniques were used to evaluate the effect of segmentation-related motion artifact (ghosting) on the multi-shot DTI data. Although both protocols generated high-resolution diffusion MRI data with correctable susceptibility-induced distortions, the single-shot protocol enables the acquisition of the high-resolution diffusion MRI data freed of ghosting and with twice the signal-to-noise ratio (SNR), for the same scan duration. The acquired chimpanzee and macaque diffusion data were used to compare the magnitude of microstructural asymmetries and age-related decline of brain white matter with those in humans. Hemispheric asymmetry results show a pattern of strong leftward asymmetry in human DTI indices that differs markedly from the chimpanzee (multi-shot data) and the rhesus macaque patterns involving both rightward and leftward asymmetries. The magnitude of leftward asymmetry increased for chimpanzees scanned with single-shot DTI sequence. Region of interest analyses within the corpus callosum revealed a significant age-related increase in fractional anisotropy (FA) in the genu for chimpanzees (multi-shot data) and no significant change in any region for macaques. Additionally, voxel-wise analysis using Tract Based Spatial Statistics (TBSS) revealed widespread age-related FA increases for chimpanzees (multi-shot data) and weak age-related decreases in FA for macaques across most white matter tracts. Overall, results from these multi-shot data analyses suggest that rhesus monkeys show age-related decreases in white matter integrity that parallel changes found in humans, whereas chimpanzees show age-related increases in white matter integrity. On the contrary, the single-shot data results for chimpanzees revealed no significant relationship between age and the different DTI indices. These noteworthy species differences may help to explain the unique features of the human mind and why humans are more susceptible to neurodegenerative diseases. Furthermore, these studies demonstrate the need for complementary histological studies of white matter microstructure in humans, chimpanzees and macaques to clarify the cellular and molecular basis of these findings.

Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 229-230). Also available in print.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 66-69). Also available in electronic version. Access restricted to campus users.

Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Duong, Tim; Committee Member: Keilholz, Shella; Committee Member: Schumacher, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Medical imaging informatics is one of the important research areas in radiology that studies how information available on medical images is retrieved, analyzed, and enhanced. Recent development in medical imaging informatics has resulted in improvement of diagnostic accuracy based on imaging examinations, as well as efficiency in clinical workflow. Computer aided diagnosis (CAD) and electronic patient record system (ePR) are both topics in medical imaging informatics that have matured from research concepts into commercially available computerized systems in clinical environment. The current challenges are to further broaden their scope of applications. In this thesis project, I developed a CAD system for interpreting PET/CT examinations and an ePR system for patient data integration in neurosurgery suites. Specifically, the CAD system in this project was designed to automatically diagnose nasopharyngeal carcinoma (NPC) on Positron emission tomography/computed tomography (PET/CT) examinations, which aimed to detect and classify both the primary NPC and its nodal metastasis. The regions of interests (ROIs) were segmented from the PET images and registered onto the CT in order to combine the imaging features from both modalities and the a priori anatomical knowledge of the suspicious lesion. These combined features were then classified by a support vector machine (SVM) to generate the final diagnosis result. The system was validated with 25 PET/CT examinations from 10 patients suffering from NPC, and the result produced by the system was compared to the gold standard of lesions manually contoured by experienced radiologists. The results confirmed that the system successfully distinguished all 53 genuine lesions from the mimickers due to normal physiological uptake and artifacts that also produced potentially confusing signals. The second part of the project involved development of an electronic patient record system (ePR) that integrated all the myriad of images and different types of clinical information before, during, and after neurosurgery operations, in order to enhance efficiency of work flow in this unique clinical environment. The system comprises of pre-, intra-, and post-operation modules which correspond to the different stages of the neurosurgery. The pre-op module was developed to store and categorize all images and data before the procedure to assist the surgeons in planning operation. The intra-op module integrates all the input signals, waveforms, images and videos that are produced by different imaging and physiological monitoring devices in the operation room during the surgery, and displays all the relevant information in a single large screen in real time to ease monitoring of the procedure. The post-op module helps surgeons to review all the data acquired from all the prior stages for follow-up of the treatment outcome. One-tumor case was utilized to test the pre-op module, and the signals and waveforms simulators were used to evaluate the performance of the intra-op module. In summary, two different medical informatics systems, a CAD and an ePR system were developed. Both showed promising results in laboratory tests. Future work would involve performance enhancement and feedback of the systems, and ultimately evaluation of these systems in the clinical environment. / published_or_final_version / Diagnostic Radiology / Master / Master of Philosophy

Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches. / Acknowledgements page removed per author's request, 01/06/2014.

Early diagnosis of disease and developing targeted therapeutics are two major goals of medical research to which nanotechnology can contribute a variety of novel approaches and solutions. This work utilized an optical phenomenon specific to metallic nanoparticles, surface-enhanced Raman spectroscopy (SERS), as a nanomedicine research tool to aid in the progression toward these goals. Single-particle SERS studies were streamlined to identify particles or aggregates with potentially high enhancement factors (EFs) for applications requiring ultrasensitive and possibly single-molecule detection. SERS was used to probe the changes in surface chemistry of nanoparticles for optimizing nanomedicine applications. Fundamental SERS imaging parameters were identified, and a new algorithm for multiplexed SERS imaging was developed and tested. Novel particle-based contrast agents were also developed. Polystyrene hollow beads with a single hole on the surface were fabricated and used to encapsulate contrast agents for a variety of medical imaging modalities. Saline was encapsulated as a novel contrast agent for thermoacoustic tomography (TAT). Encapsulation of X-ray computed tomography (CT) and magnetic resonance (MR) imaging was also performed and tested.

Electrical parameter profiles of human breast images can be used to simulate and analyze the anticipated effects on tissue from its interaction with electromagnetic fields involved in the cancer treatment exposure. In part, the success of this approach depends on the accuracy and precision in identifying the different tissue types. In this work, we propose two methods of segmenting human breast images with malignant tumors. The first method of algorithmic partitioning of the image involves manual color-edge contouring of the tissues using a cursor and subsequent identification of the tissue types. For the second method, MRI T1 values and thresholds are used to perform segmentation and we investigate the potential of incorporating edge detection. The first method is effective, while the second lacks precision, but eliminates the need of manual contouring. The images are imported as BMP files into SEMCAD, an electromagnetic simulation tool based on finite-difference time-domain method, which recognizes the grouped tissues and creates a model of the image. The model allows the user to easily assign electrical parameter values to the grouped tissues, according to the measured values reported in the literature.

Alzheimer’s disease (AD) is a progressive, degenerative neurological disorder for which there is no cure. The causative agent is ?-amyloid (A?) which becomes neurotoxic upon conformational change from ?-helix to ?-sheet. In silico methods have been used to indentify endogenous small molecules of the brain that are capable of binding to A? to inhibit conformational changes; this is a novel approach to the disease. Through the use of computational methods, several small molecules that are endogenous to the brain, such as phosphoserine, have been identified as being capable of binding to the monomeric forms of A?; in vitro studies support their role as anti-aggregants. One of the small molecules identified through these in silico methods, 3-hydroxyanthranilic acid (3HAA) has been developed through the use of Quantitative Structure-Activity Relationship (QSAR) studies to design more potent analogues. These in silico studies have also examined the capacity of synthetic compounds (structurally similar to endogenous molecules) to bind to both A? and other proteins affiliated with AD. Results indicate the potential for a single molecule to bind “promiscuously” to multiple proteins bearing a common BBXB (where B is a basic amino acid) motif affiliated with AD. This will allow for the development of molecules to target AD in a multifaceted approach. Furthermore, these small molecules can be selected through the use of “physinformatics” to bind with equal efficacy to the HHQK and LVFF regions (which play a role in the misfolding process) of A?; this will prevent conformational changes of the protein. A novel diagnostic imaging agent for AD has also been developed through computational methods; solapsone (formerly used to treat leprosy) has been identified as being structurally similar to species that bind to A? to initiate conformational changes. Results show that solapsone can chelate gadolinium, used in MRI, and bind to the soluble forms of A?, allowing for imaging of the toxic species in the human brain, and thus a definitive diagnosis of AD (which is not currently possible with living patients). Computational methods have proved useful in developing a new approach to treating AD, and designing a novel imaging agent.

Adaptive heal-doming can significantly improve the image quality in biomedical ultrasound by reducing the clutter due to interfering signals arriving from undesired directions. Adaptive beamforming is computationally expensive, and the objective of this thesis is to expose and explore tradeoffs between computational complexity and quality of adaptive beamforming. We consider the conventional linearly constrained minimum variance (LCMV) adaptive beamformer, applied to B-mode ultrasound imaging, and study an alternative based on the well-known generalized sidelobe canceller (GSC) whose adaptation relies on unconstrained gradient-driven optimization. To our knowledge, this is the first time a GSC-based gradient-driven approach has been applied and evaluated in the context of ultrasound beamforming. As another alternative to the conventional LCMV method, we also propose and evaluate a simple idea of updating the beamformer's weight vector at a reduced rate. Both approaches have lead to significant computational savings, but they also sacrifice beamforming optimality. Our simulations show that despite suboptimal beamforming. the ultrasound image quality remains acceptable.