Automated diffusion tensor tractography: implementation and comparison to user-driven tractography.
Publication/Presentation Date
5-1-2012
Abstract
RATIONALE AND OBJECTIVES: Diffusion tensor tractography offers a unique perspective of white matter anatomy, but proper delineation of white matter tracts of interest generally requires the active involvement of an expert neuroanatomist. The investigators describe the implementation of an automated tractographic method requiring no user input and compare its results to those from user-driven tractography.
MATERIALS AND METHODS: Fourteen healthy volunteers underwent diffusion tensor imaging at 3 T. Images were registered to a standard template, and predefined seed regions containing tract termini were transformed into subject space for use in unsupervised probabilistic tractography. The output was compared to the results of user-driven tractography performed on the same subjects.
RESULTS: After the selection of suitable smoothing kernels and thresholds, the results of automated tractography closely approximated those of user-driven tractography. The main bodies of the cingulum, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus were depicted equally well by both methods. Discrepancies mainly arose at the periphery of these tracts, where anatomic uncertainty tends to be greatest.
CONCLUSIONS: Automated tractography can be used to depict white matter anatomy without need for user intervention, particularly if the main body of the tract is of greatest interest.
Volume
19
Issue
5
First Page
622
Last Page
629
ISSN
1878-4046
Published In/Presented At
Nucifora, P. G., Wu, X., Melhem, E. R., Gur, R. E., Gur, R. C., & Verma, R. (2012). Automated diffusion tensor tractography: implementation and comparison to user-driven tractography. Academic radiology, 19(5), 622–629. https://doi.org/10.1016/j.acra.2012.01.002
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
22342650
Department(s)
Administration and Leadership
Document Type
Article