title: An image-processing toolset for diffusion tensor tractography.
abstract: Diffusion tensor imaging (DTI)-based fiber tractography holds great promise in delineating neuronal fiber tracts and, hence, providing connectivity maps of the neural networks in the human brain. An array of image-processing techniques has to be developed to turn DTI tractography into a practically useful tool. To this end, we have developed a suite of image-processing tools for fiber tractography with improved reliability. This article summarizes the main technical developments we have made to date, which include anisotropic smoothing, anisotropic interpolation, Bayesian fiber tracking and automatic fiber bundling. A primary focus of these techniques is the robustness to noise and partial volume averaging, the two major hurdles to reliable fiber tractography. Performance of these techniques has been comprehensively examined with simulated and in vivo DTI data, demonstrating improvements in the robustness and reliability of DTI tractography.
authors: Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232-2657, USA. arabinda.mishra@vanderbilt.edu
authors: Arabinda
authors: A
authors: Mishra
authors: Yonggang
authors: Y
authors: Lu
authors: Ann S
authors: AS
authors: Choe
authors: Akram
authors: A
authors: Aldroubi
authors: John C
authors: JC
authors: Gore
authors: Adam W
authors: AW
authors: Anderson
authors: Zhaohua
authors: Z
authors: Ding
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009415:Nerve Net / Q000033:anatomy & histology*; D009434:Neural Pathways / Q000033:anatomy & histology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software