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Front Neuroinform. 2014 May 30;8:59. doi: 10.3389/fninf.2014.00059. eCollection 2014.

Real-time multi-peak tractography for instantaneous connectivity display.

Author information

  • 1Centre de Recherche CHUS, University of Sherbrooke Sherbrooke, QC, Canada ; Department of Nuclear Medecine and Radiobiology, University of Sherbrooke Sherbrooke, QC, Canada ; Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, University of Sherbrooke Sherbrooke, QC, Canada.
  • 2Centre de Recherche CHUS, University of Sherbrooke Sherbrooke, QC, Canada ; Department of Nuclear Medecine and Radiobiology, University of Sherbrooke Sherbrooke, QC, Canada ; Department of Diagnostic Radiology, University of Sherbrooke Sherbrooke, QC, Canada.
  • 3Centre de Recherche CHUS, University of Sherbrooke Sherbrooke, QC, Canada ; Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada.
  • 4Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada.
  • 5Centre de Recherche CHUS, University of Sherbrooke Sherbrooke, QC, Canada ; Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, University of Sherbrooke Sherbrooke, QC, Canada.

Abstract

The computerized process of reconstructing white matter tracts from diffusion MRI (dMRI) data is often referred to as tractography. Tractography is nowadays central in structural connectivity since it is the only non-invasive technique to obtain information about brain wiring. Most publicly available tractography techniques and most studies are based on a fixed set of tractography parameters. However, the scale and curvature of fiber bundles can vary from region to region in the brain. Therefore, depending on the area of interest or subject (e.g., healthy control vs. tumor patient), optimal tracking parameters can be dramatically different. As a result, a slight change in tracking parameters may return different connectivity profiles and complicate the interpretation of the results. Having access to tractography parameters can thus be advantageous, as it will help in better isolating those which are sensitive to certain streamline features and potentially converge on optimal settings which are area-specific. In this work, we propose a real-time fiber tracking (RTT) tool which can instantaneously compute and display streamlines. To achieve such real-time performance, we propose a novel evolution equation based on the upsampled principal directions, also called peaks, extracted at each voxel of the dMRI dataset. The technique runs on a single Computer Processing Unit (CPU) without the need for Graphical Unit Processing (GPU) programming. We qualitatively illustrate and quantitatively evaluate our novel multi-peak RTT technique on phantom and human datasets in comparison with the state of the art offline tractography from MRtrix, which is robust to fiber crossings. Finally, we show how our RTT tool facilitates neurosurgical planning and allows one to find fibers that infiltrate tumor areas, otherwise missing when using the standard default tracking parameters.

KEYWORDS:

HARDI; diffusion MRI; free open source software; medical visualization; neurosurgical planning; tractography

PMID:
24910610
[PubMed]
PMCID:
PMC4038925
Free PMC Article

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