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Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning

1Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Volume Number:
Issue Number:
Neurosurgery. 2011 Feb; 68(2):496-505.
PubMed ID:
Diffusion Tensor Imaging, Magnetic Resonance Imaging, Neurosurgery, Surgical Planning, Tractography
Appears in Collections:
P41 RR019703/RR/NCRR NIH HHS/United States
P01 CA067165/CA/NCI NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Golby A.J., Kindlmann G., Norton I., Yarmarkovich A., Pieper S., Kikinis R. Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning. Neurosurgery. 2011 Feb; 68(2):496-505. PMID: 21135713. PMCID: PMC3112275.
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Diffusion Tensor Imaging (DTI) infers the trajectory and location of large white matter tracts by measuring anisotropic diffusion of water. DTI data may then be analyzed and presented as tractography for visualization of the tracts in three dimensions. Despite the important information contained in tractography images, usefulness for neurosurgical planning has been limited by the inability to define which are critical structures within the mass of demonstrated fibers and to clarify their relationship to the tumor. OBJECTIVE:: Our goal was to develop a method to allow the interactive querying of tractography datasets for surgical planning and to provide a working software package for the research community. METHODS:: Tool was implemented within open-source software project. Echoplanar DTI at 3T was performed on five patients, followed by tensor calculation. Software was developed allowing placement of dynamic seedpoint for local selection of fibers, and for fiber display around a segmented structure - both with tunable parameters. A neurosurgeon was trained in use of software in less than one hour and used to review cases. RESULTS:: Tracts near tumor and critical structures were interactively visualized in three dimensions to determine spatial relationships to lesion. Tracts were selected using 3 methods: (1) anatomical and fMRI-defined regions of interest (ROIs), (2) distance from the segmented tumor volume, (3) dynamic seedpoint spheres. CONCLUSION:: Interactive tractography successfully enabled inspection of white matter structures that were in proximity to lesions, critical structures, and functional cortical areas, allowing the surgeon to explore the relationships between them.

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