This thesis aimed to investigate the feasibility of two diffusion imaging techniques, Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI), on detecting subtle physiological or pathological microstructural changes in normal-appearing neural tissues of human central nervous system. 　　　At first, ten patients with neuromyelitis optica (NMO) and twelve age- and gender-matched healthy subjects were recruited. DTI-derived indices including fractional anisotropy (FA), mean diffusivity (MD), axial and radial diffusivities were quantified in the lateral and dorsal columns of cervical spinal cord. Based on the regions of interest (ROIs) measurement, NMO patients showed reduced FA, increased MD and radial diffusivity compared to control subjects, while axial diffusivity did not show any significant difference. The three former DTI metrics also showed significant correlations with disability scores, and especially FA was found to be sensitive to mild NMO. Our results show that DTI-derived indices can quantitatively assess the white matter (WM) abnormalities with seemingly normal appearance in conventional MRI, and are associated with the level of clinical disability, suggesting that DTI may have great potential as a useful diagnostic tool in the clinical setting. 　　　DKI is an extension of conventional DTI to probe the non-Gaussian diffusion property in biological tissues. Besides the four conventional DTI-derived metrics, DKI also provide three additional kurtosis metrics (mean kurtosis (MK), axial and radial kurtosis). In the second study, ROI-based analysis was used to characterize age-related microstructural changes in WM, cortical and subcortical gray matter (GM) of 27 healthy adults (21~59 yrs). Though the volumes of GM and WM were still preserved, DTI-derived metrics can detect the subtle changes in WM and GM. Meanwhile, MK and radial kurtosis significantly increased in both caudate nucleus and putamen while Thalamus showed little aging effect in the diffusivity and kurtosis metrics but significantly decreased only in FA. Our results demonstrated that DKI is sensitive to detect the age-related alterations in neural microstructures at the stage of early aging. 　　　In addition, DKI has been applied to detect the pathological changes in the normal-appearing neural tissues of 18 patients with multiple sclerosis (MS), compared to 22 healthy controls. Diffuse WM abnormalities have been observed extensively in the brain, revealed by DKI-derived metrics. Though the volumetric and voxel-wise analysis revealed no significant changes in the volume of cortical GM, decreased FA and kurtoses with increased diffusivities in MS group were sensitive to disclose the subtle alterations in global and regional cortical GM tissues. Significant correlations have been found between FA in the global, frontal and temporal cortical GM in relapsing-remitting MS patients and their disability scores, suggesting FA as an important biomarker to monitor the disease progress in cortical GM. Moreover, elevated kurtosis indices in MS patients did not correlate with diffusivities in caudate nucleus, putamen and thalamus, suggesting these metrics may be vulnerable to different pathologic aspects of the disease. 　　　In conclusion, DKI is sensitive to neural alterations during normal aging and in MS pathologies, and can provide complementary information to conventional MRI and DTI. / published_or_final_version / Diagnostic Radiology / Doctoral / Doctor of Philosophy

Volumetric analysis (VA) in magnetic resonance imaging (MRI) has provided great comprehension of the neuroanatomical changes associated to normal cognition and progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD), the most common form of dementia. However, the use of VA has primarily focused in gray matter changes; the emergence of diffusion tensor imaging (DTI) has allowed a better understanding of the microstructural changes in both white and gray matters in MCI and AD patients, with numerous studies showing DTI to be more sensitive than VA in discriminating between MCI to AD. Diffusion kurtosis imaging (DKI), an extension of DTI, is speculated to be more sensitive in detecting changes due to differences in mathematical modeling, as it accounts for non-Gaussian diffusion in the brain. Studies using DKI suggested kurtosis parameters—axial, radial, and mean kurtoses—are able to provide further microstructural details in additional information to tensor and diffusion parameters, namely axial, radial, and mean diffusivities, and fractional anisotropy. In this study, DKI is compared to DTI and VA in an attempt to evaluate the sensitivities of each technique. DKI of all four cerebral lobes and VA of the hippocampus were performed in 30 patients, 18 diagnosed with AD and 12 with MCI. Mann-Whitney U test was performed to determine differences between MCI and AD, and correlations between diffusion parameters and hippocampal volume to mini-mental state examination (MMSE) scores as a biomarker of cognitive function were tested using Pearson’s R correlation test. MMSE scores were statistically different between sexes (p = 0.025) and between MCI and AD groups (p = 0.048), as well as positively correlated with age (p = 0.004). A marginal trend was observed in the hippocampal volume between MCI and AD (p = 0.077), and did not significantly correlate with MMSE. Several diffusivity and kurtosis parameters were significantly different between MCI and AD groups in the white and gray matters of the parietal and occipital lobes. Only tensor parameters had significant negative correlations with MMSE scores in within-group analyses of the two lobes. Correlational tests of white and gray matters of all four lobes to MMSE scores revealed more significant correlations between tensor parameters than kurtosis parameters. Findings from the present study provide further evidence that diffusion MRI is a more sensitive technique than VA in the discrimination between MCI and AD. Results from this study also corroborate with another DKI study exploring diffusivity in neuroanatomical regions predominately composed of white matter in AD progression. While DKI provides additional information on the microstructural changes of white matter and gray matter during disease progression in the brain, whether DKI is superior to DTI requires further research. Diffusion MRI appears to be more advantageous when comparing cognitive function on a continuum like MMSE scores than segregated groups. / published_or_final_version / Diagnostic Radiology / Master / Master of Medical Sciences

Cervical Spondylotic Myelopathy (CSM) is a common type of spinal cord dysfunction in the elderly. The natural history of CSM is associated with disc degeneration and spondylosis, leading to the static and dynamic compression of the spinal cord, tissue ischemia, tissue damage, and ultimately neurological function deficit. However, the severity of the spinal cord compression does not necessarily correlate with the signs and symptoms of CSM in patients. Until now, the pathomechanism of CSM was not well understood. Establishing an evaluation technique is, therefore, criticalfor the pathophysiological investigation of CSM. Magnetic resonance imaging (MRI) has been widely used for evaluating the spinal cord parenchyma. However, conventional MRI is limited in detecting macroscopic changes, e.g. spinal cord compression, edema or hemorrhage etc. Recently, there has been increasing interest in diffusion tensor imaging (DTI), which permitting detects tissue water molecule diffusion at the microscopic level. The conventional DTI analysis for CSM relies on hand-drawn regions of interest (ROIs), so called ROI-based measurements. The ROIs are drawn on the sagittal image or on the axial image to cover the whole cord, which are insufficient to describe the precise diffusion pattern. In particular, the deformation and degeneration of the myelopathic cord poses a big challenge for the ROI-based analysis. The most commonly used parameter, fractional anisotropy (FA) has difficulty in determining the level diagnosis due to its relatively large variance along the cord. Furthermore, the functional activation following microstructural damage remains underexplored. In this dissertation, several novel methods for region-specific analysis were proposed for the investigation of microstructural changes in the CSM. In Chapter 2, ROI-based analysis was employed to detect the regional diffusion characteristics in CSM. In Chapter 3, an auto-template was developed that segments the cord and measures the DTI parameters automatically. We found that our auto-template outperforms hand-drawn ROI-based methods in terms of efficiency and reproducibility. In Chapter 4, entropy-based analysis was proposed to characterize the loss of complexity of microstructure in the myelopathic cord. It was demonstrated that FA entropy was an objective and quantitative evaluation parameter that was superior to conventional methods for separating CSM patients from healthy subjects. In Chapter 5, orientation entropy was used to detect the disordered orientational distribution of the nerve tracts in CSM, which could be used as a good index for the pathogenic level estimation. In Chapter 6, a diffusion tensor tractography-based method was proposed to overcome the difficulties in column-specific ROI drawing on the deformed and degenerated spinal cords. In Chapter 7, the structure-function relationship in the cervical spinal cord was explored by a combination of DTI and functional MRI. A significant correlation was found between enhanced functional responses and the loss of microstructural integrity in CSM. In this study, several novel post-processing methods were proposed and demonstrated, which were shown to have extraordinary capabilitiesfor the investigation and assessment of CSM. It is expected that these methods can be used as valuable tools for clinical diagnosis and for the selection of the most appropriate treatment strategy for CSM. / published_or_final_version / Orthopaedics and Traumatology / Doctoral / Doctor of Philosophy

No description available.

Geosynchronous (GEO) satellites are essential for modern communication networks. If communication to a GEO satellite is lost and a malfunction occurs upon orbit insertion such as a solar panel not deploying there is no direct way to observe it from Earth. Due to the GEO orbit distance of ~36,000 km from Earth's surface, the Rayleigh criteria dictates that a 14 m telescope is required to conventionally image a satellite with spatial resolution down to 1 m using visible light. Furthermore, a telescope larger than 30 m is required under ideal conditions to obtain spatial resolution down to 0.4 m. This dissertation evaluates a method for obtaining high spatial resolution images of GEO satellites from an Earth based system by measuring the irradiance distribution on the ground resulting from the occultation of the satellite passing in front of a star. The representative size of a GEO satellite combined with the orbital distance results in the ground shadow being consistent with a Fresnel diffraction pattern when observed at visible wavelengths. A measurement of the ground shadow irradiance is used as an amplitude constraint in a Gerchberg-Saxton phase retrieval algorithm that produces a reconstruction of the satellite's 2D transmission function which is analogous to a reverse contrast image of the satellite. The advantage of shadow imaging is that a terrestrial based redundant set of linearly distributed inexpensive small telescopes, each coupled to high speed detectors, is a more effective resolved imaging system for GEO satellites than a very large telescope under ideal conditions. Modeling and simulation efforts indicate sub-meter spatial resolution can be readily achieved using collection apertures of less than 1 meter in diameter. A mathematical basis is established for the treatment of the physical phenomena involved in the shadow imaging process. This includes the source star brightness and angular extent, and the diffraction of starlight from the satellite. Atmospheric effects including signal attenuation, refraction/dispersion, and turbulence are also applied to the model. The light collection and physical measurement process using highly sensitive geiger-mode avalanche photo-diode (GM-APD) detectors is described in detail. A simulation of the end-to-end shadow imaging process is constructed and then utilized to quantify the spatial resolution limits based on source star, environmental, observational, collection, measurement, and image reconstruction parameters.

No description available.

A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.

No description available.

No description available.

A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.