| Diffusion tensor imaging(DTI) scans need multiple non-collinear directions of the continuous application of diffusion-encoding gradient. Therefore, not only the size of the diffusion tensor can be measured, but also the directivity can be investigated. DTI can reflect the diffusion change in the imaging voxel more comprehensively than single diffusion-weighted image. DTI may reflect the connectivity and integrity of tissues in the brain from the microscopic point of view, and it is a non-invasive method to detect the white matter fiber tracts in vivo. DTI technology has shown great research value in a variety of pathologic analysis of lesions in the central nervous system disease s, provides an important reference for clinical treatment and prognosis, and therefore gets more and more attention.However, DTI still stay in the research stage of the brain science, and the clinical process bottlenecks mainly due to the constraints of DTI quantitative analysis. The existing DTI quantitative analytical methods to the presence have their respective application limitations. In order to establish a more accurate and reasonable DTI quantitative analysis model, and provide a reliable pathological diagnosis of diseases, we carried out the research work of this thesis. The main research contents are as follows:On the one hand, a generally accepted at present method for quantitative analysis of DTI, namely the whole brain voxel-based analysis method(VBA) is completed. Both the algorithm analysis and process are implemented. For the deficiencies of isotropic Gaussian filter method in VBA, a new anisotropic filtering algorithm is proposed to process DTI images, retaining the characteristics of the fiber bundles. And a plurality of image quality evaluation indexes were compared between the general anisotropic filtering methods and our algorithm, such as the mean square error, peak signal to noise ratio, structural similarity, etc. The superiority of the proposed algorithm has been demonstrated.On the other hand, as an increasingly popular DTI quantitative analysis method in recent years, tract-based spatial statistics(TBSS) of has been studied and implemented. Combined with the new improved VBA method based on the proposed anisotropic filter algorithm, a new DTI quantitative analysis fusion model was built. Integrated the analysis strengths of the two methods, the new model can more accurately locate the white matter fiber tracts where DTI parameters’ values were significantly changed, and it also can analyze changes in the overall structure of the lesion area. That is the new VBA and TBSS fusion quantitative analysis model.The model is finally applied to the analysis of clinical multiple sclerosis(MS) DTI data, and a relatively result is obtained. Significant changes in DTI parameters of optic fiber bundles were found, which indicated that the optic nerve bundles’ myelin maybe damaged or axonal injury occurred. This is in line with the common clinical symptoms of visual disability in MS patients. And such lesions cannot be seen on MRI scans. It is an important discovery in prognosis and early clinical diagnosis of multiple sclerosis. |
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