Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution

Hum Brain Mapp. 2011 Mar;32(3):461-79. doi: 10.1002/hbm.21032.


Constrained spherical deconvolution (CSD) is a new technique that, based on high-angular resolution diffusion imaging (HARDI) MR data, estimates the orientation of multiple intravoxel fiber populations within regions of complex white matter architecture, thereby overcoming the limitations of the widely used diffusion tensor imaging (DTI) technique. One of its main applications is fiber tractography. The noisy nature of diffusion-weighted (DW) images, however, affects the estimated orientations and the resulting fiber trajectories will be subject to uncertainty. The impact of noise can be large, especially for HARDI measurements, which employ relatively high b-values. To quantify the effects of noise on fiber trajectories, probabilistic tractography was introduced, which considers multiple possible pathways emanating from one seed point, taking into account the uncertainty of local fiber orientations. In this work, a probabilistic tractography algorithm is presented based on CSD and the residual bootstrap. CSD, which provides accurate and precise estimates of multiple fiber orientations, is used to extract the local fiber orientations. The residual bootstrap is used to estimate fiber tract probability within a clinical time frame, without prior assumptions about the form of uncertainty in the data. By means of Monte Carlo simulations, the performance of the CSD fiber pathway uncertainty estimator is measured in terms of accuracy and precision. In addition, the performance of the proposed method is compared to state-of-the-art DTI residual bootstrap tractography and to an existing probabilistic CSD tractography algorithm using clinical DW data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / anatomy & histology
  • Brain / physiology*
  • Brain Mapping*
  • Computer Simulation
  • Diffusion Magnetic Resonance Imaging
  • Humans
  • Image Processing, Computer-Assisted
  • Models, Neurological
  • Nerve Fibers / physiology*
  • Pattern Recognition, Automated
  • Probability*
  • Sensitivity and Specificity