Room 355 BC

16:00 - 18:00

Moderators:

Maxime Descoteaux, Carl-Fredrik Westin

Justin P. Haldar¹, David W. Shattuck², and Richard M. Leahy^1
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.

Jacques-Donald Tournier^1,2, Fernando Calamante^2,3, and Alan Connelly^1,2
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.

Matthew Rowe¹, Hui Zhang¹, and Daniel C. Alexander^1
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.

Thijs Dhollander^1,2, Louise Emsell^1,3, Wim Van Hecke¹, Frederik Maes^1,2, Stefan Sunaert^1,3, and Paul Suetens^1,2
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.

Gabriel Girard¹ and Maxime Descoteaux^1
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.

Yong Wang¹, Peng Sun¹, Fang-Chang Yeh², Robert Naismith^3,4, Anne H. Cross^3,4, and Sheng-Kwei Song^1,4
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.

Christian Ros^1,2, Daniel Guellmar¹, Martin Stenzel², Hans-Joachim Mentzel², and Jürgen R. Reichenbach^1
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.

Greg D. Parker¹, David Marshall², Paul L. Rosin², Nicholas Drage³, Stephen Richmond³, and Derek K. Jones^1
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.

Edison Pardo¹, Pamela Guevara¹, Delphine Duclap², Josselin Houenou², Alice Lebois², Benoit Schmitt², Denis Le Bihan², Jean-François Mangin², and Cyril Poupon^2
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.

Longchuan Li¹, Xiaoping P. Hu², Todd Preuss^3,4, Matthew F. Glasser⁵, Frederick William Damen², Yuxuan Qiu⁶, and James Rilling^4,7
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.