A TECHNICAL FRAMEWORK FOR GROUP STUDIES OF DIFFUSION TENSOR IMAGING

2022-8-23
METIN, Mehmet Ozer
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Diffusion tensor imaging (DTI) is an ideal tool to investigate white matter abnormalities. In this study, novel techniques that use non-scalar metrics have been proposed for group-based DTI analysis. Utilization of directional statistics to evaluate group differences is the main achievement of this thesis. Directional statistics can encapsulate much more information than scalar metrics about the diffusion tensors extracted from groups of diffusion weighted images. We have introduced two new approaches to analyze group differences. The first method augments probabilistic fiber tractography with a new visualization technique to carry out group-based DTI analysis for connectivity-based hypothesis testing. Probabilistic fiber tractography is extended with a new method to visualize FA values versus arc-length. This method not only enables hypothesis testing of probabilistic tracts but also provides multi-resolution visualization. The second method introduces a new technique called tract profiling and directional statistics (TPDS). We have investigated different directional statistical models to find the best fit. During the experiments, we confirmed that carrying out directional statistical analysis along the tract is much more effective than voxel- or skeleton-guided directional statistics. As a case study, the method has been applied to identify connectivity differences of patients with major depressive disorder. The results obtained with the directional statistic-based analysis are consistent with those of Network Based Statistics (NBS), but additionally, we found significant changes in the right hemisphere striatum, ACC, and prefrontal, parietal, temporal, and occipital connections as well as left hemispheric differences in the limbic areas such as the thalamus, amygdala, and hippocampus. Comparison with the output of the network-based statistical toolbox indicated that the benefit of the proposed method becomes much more distinctive as the tract length increases.
Diffusion tensor imaging, group based dti analysis, directional statistics
https://hdl.handle.net/11511/98786
Graduate School of Informatics, Thesis

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Citation Formats
M. O. METIN, “A TECHNICAL FRAMEWORK FOR GROUP STUDIES OF DIFFUSION TENSOR IMAGING,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.
METU IIS - Integrated Information Service open@metu.edu.tr