MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING

Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts. The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain. Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data

Altered Posterior Cerebellar Lobule Connectivity With Perigenual Anterior Cingulate Cortex in Women With Primary Dysmenorrhea

Objectives: This study aimed to investigate the potential connectivity mechanism between the cerebellum and anterior cingulate cortex (ACC) and the cerebellar structure in primary dysmenorrhea (PDM).Methods: We applied the spatially unbiased infratentorial template (SUIT) of the cerebellum to obtain anatomical details of cerebellar lobules, upon which the functional connectivity (FC) between the cerebellar lobules and ACC subregions was analyzed and the gray matter (GM) volume of cerebellar lobules was measured by using voxel-based morphometry (VBM) in 35 PDM females and 38 age-matched healthy females. The potential relationship between the altered FC or GM volume and clinical information was also evaluated in PDM females.Results: PDM females showed higher connectivity between the left perigenual ACC (pACC) and lobule vermis_VI, between the left pACC and left lobule IX, and between right pACC and right cerebellar lobule VIIb than did the healthy controls. Compared with healthy controls, no altered GM volume was found in PDM females. No significant correlation was found between altered cerebellum–ACC FC and the clinical variables in the PDM females.Conclusion: PDM females have abnormal posterior cerebellar connectivity with pACC but no abnormal structural changes. ACC–cerebellar circuit disturbances might be involved in the PDM females

GCA: a Coclustering Algorithm for Thalamo-CorticoThalamic Connectivity Analysis

The reciprocal connectivity between the cerebral cortex and the thalamus in a human brain is involved in consciousness and related to various brain disorders, thus, in-vivo analysis of this connectivity is critically important for brain diagnosis and surgery planning. While existing work either focuses on fiber tracking analysis or on thalamic nuclei segmentation, to our best knowledge, no techniques yet exist for performing in-vivo analysis of thalamo-corticothalamic connectivity. In this paper, (i) we propose a new partitioning paradigm, called coclustering, to model this problem. In contrast to the traditional clustering paradigm, a coclustering procedure not only simultaneously partitions cortical voxels and thalamic voxels into groups, but also identifies the corresponding strong connectivities between the two classes of groups; (ii) we develop the first coclustering algorithm, Genetic Coclustering Algorithm (GCA), to solve the coclustering problem; and (iii) we apply GCA to perform in-vivo analysis of the thalamo-cortico-thalamic connectivity and produce a strikingly clear 3-D visualization of the seven thalamic nuclei groups as well as their connectivities to the corresponding cortical regions of a human brain.

A framework for quantitative and visual analysis of white matter integrity using Diffusion Tensor Imaging

A new quantitative and visual analysis technique is developed to study the white matter integrity along fibre bundles using Diffusion Tensor Imaging (DTI) tractography. Our approach takes advantages of DTI tractography and geodesic path mapping, which establishes group-wise correspondences to allow direct cross-subject evaluation of diffusion properties. The 3D volumetric fibre bundle is approximated by streamlines reconstructed using DTI. We present the fibre bundle mask strategy to measure thin fibre bundles in group analysis. A novel isonode visualisation method is developed to facilitate the visual inspection of local white matter alterations within a neural pathway

A Time-Optimal Intersection Search Algorithm for Robot Grasping

In industrial automation, an important task of robot system is to grasp moving objects. In order to grasp the moving target on the conveyor belt in the shortest time, this paper proposes a method of location prediction and interception grasping of the target object, and puts forward the corresponding time-optimal grasping point search algorithm. In order to reduce the motion impact of the manipulator and meet the kinematic constraints, the velocity planning curve of the normalized quintic polynomial was adopted to plan the motion of the manipulator in the joint space. In order to express the algorithm more clearly, the position-time function of the moving object and the position-time virtual function of the robot are introduced, and the time optimal grasping point appears at the intersection of the two functions. Finally, the method is verified by simulation analysis

Computing White Matter Fiber Orientations in High Angular Resolution Diffusion-Weighted MRI

Diffusion tensor magnetic resonance imaging (DTI) can resolve white matter fiber orientations within voxels in which the diffusion is characterized by Gaussian diffusion process. High angular resolution diffusion imaging (HARDI) adds the capability of describing the apparent diffusion coefficient (ADC) profile of voxels that contain kissing, branching and crossing fiber configurations. We present a new method for recovering BiGaussian model from HARDI. This method divides the parameters of biGaussian model into the parameters describing the shape of ADC profile and the parameters defining the orientation of ADC profile. The two types of parameters are recovered in separate steps