title: Knowledge-based automated reconstruction of human brain white matter tracts using a path-finding approach with dynamic programming.
abstract: It has been shown that the anatomy of major white matter tracts can be delineated using diffusion tensor imaging (DTI) data. Tract reconstruction, however, often suffers from a large number of false-negative results when a simple line propagation algorithm is used. This limits the application of this technique to only the core of prominent white matter tracts. By employing probabilistic path-generation algorithms, connectivity between a larger number of anatomical regions can be studied, but an increase in the number of false-positive results is inevitable. One of the causes of the inaccuracy is the complex axonal anatomy within a voxel; however, high-angular resolution (HAR) methods have been proposed to ameliorate this limitation. However, HAR data are relatively rare due to the long scan times required and the low signal-to-noise ratio. In this study, we tested a probabilistic path-finding method in which two anatomical regions with known connectivity were pre-defined and a path that maximized agreement with the DTI data was searched. To increase the accuracy of the trajectories, knowledge-based anatomical constraints were applied. The reconstruction protocols were tested using DTI data from 19 normal subjects to examine test-retest reproducibility and cross-subject variability. Fifty-two tracts were found to be reliably reconstructed using this approach, which can be viewed on our website.
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China.
authors.forename: Muwei
authors.initials: M
authors.lastname: Li
authors.affiliation: Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: J Tilak
authors.initials: JT
authors.lastname: Ratnanather
authors.affiliation: Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated; D012984:Software; D055815:Young Adult