뇌 구조 파라미터 모델을 이용한 대뇌 피질의 평균확산도 매핑 방법 및 응용에 관한 연구

Abstract

Diffusion tensor imaging (DTI) is considered an important technique to understand the microstructural changes of the human brain in relation to neuropathological disorder. Fractional anisotropy (FA) and mean diffusivity (MD) are the most widely used as scalar measurements to characterize diffusion properties. In most of studies, DTI is used widely for the investigation white matter (WM) abnormality associated with neuropathological disorder. Although these DTI studies are useful in investigation of the abnormality occurring on brain WM, many neurodegenerative disorders such as Alzheimer’s disease (AD), primarily is related to the GM. Several studies have used the MD value in the cortical GM to detect the characteristics of specific diseases. Recently, researchers have applied a cortical surface generated from the T1-weighted volume to analyze cortical GM. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. In this dissertation, I introduced a Surface Guided Diffusion Mapping (SGDM) method to compensate for mapping error and investigated structural abnormalities in cortical GM in patients with AD patients compared with those in the old healthy group using the method. I compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. I also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The cerebrospinal fluid (CSF) probability values provided by the SGDM method were lower than those produced by the NN and Linear method. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the SGDM approach is better than the NN and Linear methods in the absence of additional information about geometric distortions, such as a field map. In clinical application study, I founded significantly elevated MD in AD patients in the entorhinal cortex, parahippocampal gyrus, temporal gyrus, precentral gyrus and posterior cingulate cortex. The results indicated the elevation of MD value in AD patients might be the result of microstructural GM degeneration. The results also indicated that the degenerative processes occurring in AD patients are initially well localized in the entorhinal cortex, and then spread into other cortical regions as reported in previous studies. In consequence, this dissertation suggests that the SGDM method is an effective way to correct mapping errors generated from geometric distortion and demonstrates the ability of the SGDM method to detect changes in cortical GM in AD, across the entire brain.