ISMRM 21st Annual Meeting & Exhibition ○ 20-26 April 2013 ○ Salt Lake City, Utah, USA
SCIENTIFIC SESSION
Fibers & Tractography Thursday 25 April 2013
Room 355 BC | 16:00 - 18:00 | Moderators: | Maxime Descoteaux, Carl-Fredrik Westin
16:00 | 0771. | Estimation of White Matter Fiber Orientations with the Funk-Radon and Cosine Transform Justin P. Haldar1, David W. Shattuck2, and Richard M. Leahy1 1Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, 2Laboratory of Neuro Imaging, University of California, Los Angeles, CA, United States Tractography methods depend on estimating orientation distribution functions (ODFs) from diffusion MRI data. This work evaluates the performance of a new ODF estimation method known as the Funk-Radon and Cosine Transform (FRACT). The FRACT is a linear transformation technique for spherically-sampled q-space data that generalizes the previous Funk-Radon Transform (FRT). It estimates the constant solid angle ODF, can be characterized theoretically, can be computed efficiently, and substantially outperforms the FRT. This work compares the FRACT to existing ODF estimation methods with simulated and real data. Results demonstrate that the FRACT can be a powerful tool for MR tractography applications.
16:12 | 0772. | A Robust Spherical Deconvolution Method for the Analysis of Low SNR or Low Angular Resolution Diffusion Data Jacques-Donald Tournier1,2, Fernando Calamante2,3, and Alan Connelly1,2 1Advanced MRI development, The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia, 2Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia, 3Advanced MRI development, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia Analysis of low SNR, low b-value, or low angular resolution DWI data is difficult using HARDI methods such as spherical deconvolution. We propose to improve the robustness of spherical deconvolution to handle such data by including Rician correction and a constraint on the smoothness along fibres along with the commonly-used non-negativity constraint. We demonstrate this method on the types of data mentioned above, and show significant improvements in the quality of the reconstruction.
16:24 | 0773. | ND-Track: Tractography Utilising Parametric Models of White Matter Fibre Orientation Dispersion Matthew Rowe1, Hui Zhang1, and Daniel C. Alexander1 1Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom We propose a new tractography algorithm leveraging parametric models of dispersion fit to diffusion weighted magnetic resonance imaging to guide streamline propagation probabilistically. Many current tractography techniques rely on a few discrete directions per voxel which can misrepresent the underlying anatomy, opening a risk of false negative connections. We test the algorithm on synthetic data and in vivo data of a human subject. The algorithm shows advantages in tracking through the corona radiata, a region of white matter known to exhibit a significant degree of fiber dispersion. We also demonstrate that the algorithm succeeds in tracking the major white matter pathways for which standard techniques work well.
16:36 | 0774. | Robustifying Probabilistic Tractography by Using Track Orientation Distributions Thijs Dhollander1,2, Louise Emsell1,3, Wim Van Hecke1, Frederik Maes1,2, Stefan Sunaert1,3, and Paul Suetens1,2 1Medical Imaging Research Center (MIRC), KU Leuven, Leuven, Belgium, 2Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium, 3Translational MRI, KU Leuven, Leuven, Belgium We propose to extend the concept of track-density imaging (TDI) to also encode the angular distribution of a dense full-brain short-tracks tractogram. Similarly to the fiber orientation distribution (FOD), the resulting track orientation distribution (TOD) in each voxel has peaks in the general directions of white matter pathways. As such, the TOD can be used anew to generate a short-tracks tractogram, yielding another TOD. We explore the meaning and effectiveness of using these TODs for (targeted) tractography. We show that, by inherently "planning ahead", the TODs are able to guide the tractography process much more robustly.
16:48 | 0775. | Online Filtering Tractography: Tracking with Anatomical Priors Gabriel Girard1 and Maxime Descoteaux1 1Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada This abstract investigates how the mask affects streamline tractography. The discrete binary mask is an aggressive stopping criterion that can result in a large proportion of prematurely stopping streamlines [1]. We propose a method called Online Filtering Tractography (OFT) which propagates simultaneously multiple streamlines using the full partial volume fraction maps to enforce the tracking in the white matter and stop in gray matter. Streamlines propagating in cerebrospinal fluid partial volume fraction map are iteratively repressed. Results of OFT using partial volume fraction maps overcome some limits of tracking with binary mask.
17:00 | 0776. | Tractography with Physiology Rendering of Human Brain Using Diffusion Basis Spectrum Imaging Yong Wang1, Peng Sun1, Fang-Chang Yeh2, Robert Naismith3,4, Anne H. Cross3,4, and Sheng-Kwei Song1,4 1Radiology, Washington University in St. Louis, Saint Louis, MO, United States, 2Departments of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States, 3Neurology, Washington University in St. Louis, Saint Louis, MO, United States, 4Hope Center of Neurological disorders, Washington University in St. Louis, Saint Louis, MO, United States Diffusion tensor imaging (DTI) has been successfully used to quantify directional diffusivities of coherent white matter tracts and perform tractography. However, DTI cannot model crossing fibers and subvoxel partial volume effect due to increased cellularity and extra-cellular space. Diffusion basis spectrum imaging (DBSI) has recently been proposed to overcome DTI limitations. Preliminary phantom and animal studies have suggested that DBSI not only resolved crossing fibers, but also computed directional diffusivities of each crossing fiber and quantified subvoxel partial volume effect. In this study, we reported the first application of DBSI to normal human brain and demonstrated DBSI utilities mapping white matter connectivity and quantifying multiple diffusion components along fiber tracts.
17:12 | 0777. | Atlas-Guided Cluster Analysis of Fiber Tracts Christian Ros1,2, Daniel Guellmar1, Martin Stenzel2, Hans-Joachim Mentzel2, and Jürgen R. Reichenbach1 1Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, TH, Germany, 2Pediatric Radiology, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, TH, Germany With this contribution we present a new hybrid approach that incorporates anatomic information of a probabilistic white matter fiber bundle atlas into the cluster analysis. This technique enables the robust and consistent extraction of fiber bundles that correspond to the classes in the atlas. In addition, it offers the advantage to identify bundles in the data set that are not defined in the atlas. To validate the robustness and the consistent extraction for multiple subjects, data sets of 46 healthy subjects were processed and atlas-guided clustering was performed.
17:24 | 0778. | Fast and Fully Automated Clustering of Whole Brain Tractography Results Using Shape-Space Analysis Greg D. Parker1, David Marshall2, Paul L. Rosin2, Nicholas Drage3, Stephen Richmond3, and Derek K. Jones1 1CUBRIC, School of Psychology, Cardiff University, Cardiff, South Glamorgan, United Kingdom, 2School of Computer Science, Cardiff University, Cardiff, South Glamorgan, United Kingdom, 3School of Dentistry, Cardiff University, Cardiff, South Glamorgan, United Kingdom We propose a novel method for fully automated segmentation of large tractography datasets. By measuring the modes and magnitudes of streamline shape variation within the brain, we are able to build a white matter shape space in which streamlines belonging to particular anatomical features consistently project to distinct sub-regions; thus allowing us to segment unseen streamline data by observing their projected positions. An additional advantage of this technique is the computationally trivial nature of the projection process which, when compared to other techniques with similar aims, significantly reduces both run time and memory footprint.
17:36 | 0779. | Study of the Variability of Short Association Bundles Segmented Using an Automatic Method Applied to a HARDI Database. Edison Pardo1, Pamela Guevara1, Delphine Duclap2, Josselin Houenou2, Alice Lebois2, Benoit Schmitt2, Denis Le Bihan2, Jean-François Mangin2, and Cyril Poupon2 1University of Concepción, Concepción, Concepción, Chile, 2I2BM, CEA-Neurospin, Gif-sur-Yvette, France, France The construction of an atlas of the human brain connectome, in particular, the cartography of fiber bundles of superficial WM is a complex an unachieved task. In this work we applied an automatic WM segmentation method proposed in the literature for the analysis of variability analysis of a big amount of superficial WM bundles. The method was applied to 20 subjects of a HARDI high quality database, adding several processing steps in order to improve the results. Then we studied the variability of 40 SWM fiber bundles from each hemisphere, and we constructed a model of these bundles in MNI space.
17:48 | 0780. | Mapping Putative Centrality Hubs in Rhesus Macaques and Humans Using Diffusion Tractography and Graph Theory Longchuan Li1, Xiaoping P. Hu2, Todd Preuss3,4, Matthew F. Glasser5, Frederick William Damen2, Yuxuan Qiu6, and James Rilling4,7 1Biomedical Imaging Technology Center, Emory University/Georiga Tech, Atlanta, GA, United States, 2Department of Biomedical Engineering, Emory University/Georgia Tech, Atlanta, GA, United States, 3Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States, 4Center for Translational and Social Neuroscience, Emory University, Atlanta, GA, United States, 5Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, United States, 6School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States, 7Department of Anthropology, Emory University, Atlanta, GA, United States Although brain networks derived via diffusion tractography have been widely used in ascertain brain’s structural connectivity, the accuracy of the networks has yet to be fully validated. We compared tractography- and tracer-derived brain networks of monkeys for evaluation purposes as well as the tractography-derived networks of monkeys and humans for insight into interspecies differences. A relatively good correspondence between the tracer- and tractography-derived brain networks of monkeys was noted. When comparing the networks from the two species, we found common hubs in the medial parietal cortex, but a discrepancy in the medial prefrontal cortex.