METIN, Mehmet Ozer
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.


A Parametric Estimation Approach to Instantaneous Spectral Imaging
Öktem, Sevinç Figen; Davila, Joseph M (2014-12-01)
Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging techn...
An Information theoretic representation of brain connectivity for cognitive state classification using functional magnetic resonance imaging
Önal, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2013)
In this study, a new method for analyzing and representing the discriminative information, distributed in functional Magnetic Resonance Imaging (fMRI) data, is proposed. For this purpose, a local mesh with varying size is formed around each voxel, called the seed voxel. The relationships among each seed voxel and its neighbors are estimated using a linear regression equation by minimizing the expectation of the squared error. This squared error coming from linear regression is used to calculate various info...
Full-Wave Computational Analysis of Optical Chiral Metamaterials
Guler, Sadri; Solak, Birol; Gür, Uğur Meriç; Ergül, Özgür Salih (2017-09-27)
We present computational analysis of optical chiral metamaterials that consist of helical metallic elements. At optical frequencies, metals are modeled as penetrable objects with plasmonic properties. A rigorous implementation based on boundary element methods and the multilevel fast multipole algorithm is used for efficient and accurate analysis of three-dimensional structures. Numerical results demonstrate interesting polarization-rotating characteristics of such arrays with helical elements, as well as t...
A General framework for adaptive radar detection based on fast and slow-time preprocessing
Saraç, Uğur Berkay; Güvensen, Gökhan Muzzaffer.; Department of Electrical and Electronics Engineering (2019)
This thesis is about the design of an adaptive radar detector under heterogeneous clutter environment using a small number of secondary data, which is at the same time robust to Doppler mismatch. To this end, the observations taken from heterogeneous clutter environment are first processed with a specially designed fast-time preprocessing matrix, cleansing the target contamination in the secondary range cells. Using these clean secondary data, the covariance matrix of the clutter is estimated via the parame...
A Proposed ground motion and scaling procedure for structural systems
Ay, Bekir Özer; Akkar, Sinan; Department of Civil Engineering (2012)
This study presents a ground-motion selection and scaling procedure that preserves the inherent uncertainty in the modified recordings. The proposed procedure provides a set of scaled ground-motion records to be used in the response estimation of structural systems for a pre-defined earthquake hazard level. Given a relatively larger ground-motion dataset, the methodology constrains the selection and scaling of the accelerograms to the differences between individual records and corresponding estimations from...
Citation Formats
M. O. METIN, “A TECHNICAL FRAMEWORK FOR GROUP STUDIES OF DIFFUSION TENSOR IMAGING,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.