Diffusion: Tracts & Tracking Methods
Friday 24 April 2009
Room 316BC 10:30-12:30


Thomas E. Conturo and Jacques-Donald Tournier

10:30 852. Graph-Based Tractography for Robust Propagation Through Complex Fibre Configurations
    Stamatios N. Sotiropoulos1, Li Bai2, Paul S. Morgan3,4, Christopher R. Tench1
Division of Clinical Neurology, University of Nottingham, Nottingham, UK; 2School of Computer Science, University of Nottingham, Nottingham, UK; 3Division of Academic Radiology, University of Nottingham, Nottingham, UK; 4Radiology & Radiological Sciences, Medical University of South Carolina, Charleston, SC, USA
    Graph-based distributed tractography provides an alternative to streamline approaches. However, graph-based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations. In this study, we discuss these limitations and present a new approach for robustly propagating through fibre crossings, as these are depicted by the Q-ball orientation distribution functions (ODFs).Complex ODFs are decomposed to components representative of single-fibre populations and an appropriate image graph is created. Path strengths are calculated using a modified version of Dijkstra’s shortest path algorithm. A comparison with existing methods is performed on simulated and on human Q-ball imaging data.
10:42 853. ICA Based Multi-Fiber Tractography
    Manbir Singh1, Chi-Wah Wong1
Radiology and Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
    ICA was used to estimate orientations of up to 3 fibers per voxel and conduct streamline whole-brain tractography. Simulation studies suggest an accuracy of about 10 deg for 2 fibers, around 15 deg for 3 fibers, and that ICA is about 100-times faster than multi-tensor compartmental models. Experimental data suggest the residual error between ICA estimated orientations and measured diffusion profiles is 100-times smaller than multi-tensor models. Comparison of ICA and PCA to recover the fornix and cingulum tracts from streamline whole-brain tractography in humans using identical ROIs show better tract continuity and branching with ICA than PCA.
10:54 854. A Hough Transform Global Approach to Diffusion MRI Tractography
    Iman Aganj1, Christophe Lenglet1,2, Renaud Keriven3, Guillermo Sapiro1, Noam Harel2, Paul Thompson4
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA; 2Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA; 3CERTIS, École Nationale des Ponts et Chaussées, Champs-sur-Marne, Marne-la-Vallée, France; 4Laboratory of Neuro Imaging, University of California-Los Angeles, Los Angeles, CA, USA
    Tractography algorithms based on local fiber orientation estimates are vulnerable to noise, partial volume effects, and above all the fiber crossing, since recovering connectivity in regions where fiber bundles mingle is particularly difficult. In this work, we present a global approach based on Hough transform. Our tractography algorithm essentially tests all possible 3D curves in the volume while giving a score to each of them, then chooses the curves with the highest scores and returns them as the potential connections. We present experimental results on both artificial and real DTI and HARDI data.
11:06 855. A New Approach to Fully Automated Fiber Tract Clustering Using Affinity Propagation
    Alexander Leemans1, Derek K. Jones1
CUBRIC, School of Psychology, Cardiff University, Cardiff, Wales, UK
    In this work, we present a novel approach to fiber tract clustering approach on the recently introduced concept of ‘affinity propagation’ (AP). In contrast to other clustering methods, AP clustering allows one to (i) produce tract exemplars; (ii) incorporate asymmetric tract distance measures (e.g., Hausdorff metric); and – importantly – (iii) determine the number of clusters automatically. Here, we demonstrate 1) the superior performance of AP over spectral and hierarchical clustering methods and 2) how the AP method improves atlas-based tract segmentations.
11:18 856. Automatic Segmentation of White Matter Structures from DTI Using Tensor Invariants and Tensor Orientation
    Rodrigo de Luis Garcia1,2, Carlos Alberola Lopez2, Gordon Kindlmann1, Carl-Fredrik Westin1
Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA; 2Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
    This abstract presents a fully automatic DTI segmentation method for anatomical structures in the white matter. Our approach is based on: (a) the use of tensor invariants and the orientation information of the tensor as features, (b) a statistical modeling of the data with a level set implementation, and (c) an automatic initialization with a DTI white matter atlas. This formulation allows to control the relative importance of the different properties of the diffusion tensor, which overcomes limitations of previous approaches in the literature. The method has been validated on two DTI volumes, showing accurate and robust results.
11:30 857. Semantic and Phonological Processing in the Left Inferior Frontal Gyrus: Observations from a Combined Distortion Corrected FMRI and Tractography Study
    Karl V. Embleton1,2, Geoff J. Parker1, David M. Morris1, Hamied A. Haroon1, Matt A. Lambon Ralph2
Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, University of Manchester, Manchester, UK; 2School of Psychological Sciences, University of Manchester, Manchester, UK
    A combined semantic/language fMRI and tractography study was performed on 12 individuals. Functional activations were used to directly seed probabilistic tractography. The functional data revealed a left hemisphere dominance in task related activity with substantial activation in brain regions known to be associated with semantic and/or language processing. Functional activations in the inferior frontal lobe could be differentially related to language or semantic memory by examination of patterns of connectivity to other brain regions. The incorporation of tractography suggested an anterior to posterior gradient of semantic-language processing within the left inferior frontal lobe, in keeping with recent reports.
11:42 858. Predicting Resting-State Functional Connectivity from Structural Connectivity
    Chris J. Honey1, Olaf Sporns1, Leila Cammoun2, Xavier Gigandet2, Jean-Philippe Thiran2, Reto Meuli3, Patric Hagmann2,3
1 Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; 2Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland; 3Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, VD, Switzerland
    The patterns of functional connectivity across the brain are presumed to reflect its underlying structural (anatomical) architecture. In the present study, we measured resting state functional connection patterns (using fMRI) and structural connection patterns (using DSI) in the same individuals. Structural connectivity then provided the couplings for a model of macroscopic linear and non-linear cortical dynamics. In both models, (i) we where able to infer functional connectivity from structural data with strong accuracy (ii) the correlations between simulated and empirical rsFC were highest for many regions located in the default mode network.
11:54 859. Fully Automated Probabilistic White-Matter Tractography with Anatomical Priors: Application to Huntington's Disease
    Anastasia Yendiki1, Allison Stevens1, Jean Augustinack1, David Salat1, Lilla Zollei1, Ruopeng Wang1, Diana Rosas2, Bruce Fischl1,3
HMS/MGH/MIT Martinos Center for Biomedical Imaging, Charlestown, MA, USA; 2Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; 3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
    We illustrate the application of a robust and fully automated method for probabilistic white-matter tractography to analyze diffusion-weighted MR images from a large cohort of Huntington's disease patients and matched healthy controls. The method uses manually labeled paths from a set of training subjects to construct priors on these paths. The priors are then incorporated into a probabilistic tractography framework to trace the paths automatically in the test subjects (Huntington's disease patients and controls). Preliminary results show significant decreases of the fractional anisotropy in several parts of the corticospinal tract and superior longitudinal fasciculus of the patient population.
12:06 860. Asymmetries in Chimpanzees (Pan Troglodytes) Corticospinal System - A Diffusion Magnetic Resonance Imaging (MRI) Study
    Longchuan Li1, Todd M. Preuss2, James K. Rilling3,4, William D. Hopkins2, Matthew F. Glasser, Bhargav Kumar5, Roger Nana5, Xiaodong Zhang2, Xiaoping Hu1,5
Biomedical Imaging Technology Center, School of Medicine, Emory University, Atlanta, GA, USA; 2Division of Neuroscience, Yerkes National Primate Research Center, Atlanta, GA, USA; 3Department of Anthropology, Emory University, Atlanta, GA, USA; 4Division of Psychobiology, Yerkes National Primate Research Center, Atlanta, GA, USA; 5Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, USA
    Diffusion MRI was used to explore chimpanzees’ asymmetries in corticospinal system and their relationship with behavioral measures of handedness. Our results show that significant hemispheric asymmetries were observed at different levels of the corticospinal system in chimpanzees. Probabilistic tractography results suggest that the asymmetry in chimpanzees’ corticospinal system might be a combined result of the difference in hemispheric cortical connectivity and the asymmetry in white matter microstructure. A significant positive correlation between the asymmetry quiotent derived using DTI measure at the postcentral gyrus (PoG) and handedness indicates that the white matter microstructural asymmetry at the PoG reflects the functional lateralization of chimpanzees’ corticospinal system.
12:18 861. Diffusion Tensor Tractography of Individual Nerve Fibers in the Ventral Spinal Cord of the Rat with Histological Validation
    Jeremy J. Flint1,2, Brian Hansen3, Michael Fey4, Daniel Schmidig4, Michael A. King5, Peter Vestergaard-Poulsen3, Stephen J. Blackband1,6
Neuroscience, University of Florida, Gainesville, FL, USA; 2McKnight Brain Institute, University of Florida, Gainesville, FL, USA; 3Center of Functionally Integrative Neuroscience, University of Aarhus, Aarhus, Denmark; 4Bruker Biospin AG, Switzerland; 5Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA; 6National High Magnetic Field Laboratory, Tallahassee, FL, USA
    Diffusion tensor tractography (DTT) uses data gleaned from DTI experiments to elucidate the path taken by white matter tracts through multiple image voxels. Although numerous methodologies have been employed to perform this function, no method has been proposed as a means of unambiguously verifying the accuracy of DTT data. We propose a method of validating DTT data by comparing tractography maps generated using magnetic resonance microscopy to correlative histology of the same tissue. By these methods, the orientations of white-matter tracts are clearly represented in the histological images which can be used as a template for verification of DTT-generated tracts.