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

Indiana University-Purdue University Indianapolis (IUPUI) / Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.