Estimation of uncertainty in constrained spherical deconvolution fiber orientations

Ben Jeurissen*, Alexander Leemans, Jacques-Donald Tournier, Jan Sijbers

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

5 Citations (Scopus)

Abstract

Constrained spherical deconvolution (CSD) is a new reconstruction technique that extracts white matter fiber orientations from diffusion weighted MRI data of the brain. However, since these orientations are estimated from noisy data, they are subject to errors, which propagate during fiber tractography. Therefore, it is important to estimate the uncertainty associated with the fiber orientations. In this work, we investigate the performance of a statistical method called the bootstrap, when estimating confidence intervals for CSD fiber orientations. The bootstrap is a nonparametric statistical technique based on data resampling. We used Monte Carlo simulations to measure both its accuracy and precision when applied to CSD. Also, we evaluated an alternative method called the bootknife, which aims to increase the precision of the bootstrap.

Original languageEnglish
Title of host publication2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4
Place of PublicationNEW YORK
PublisherIEEE
Pages907-910
Number of pages4
ISBN (Print)978-1-4244-2002-5
Publication statusPublished - 2008
Event5th IEEE International Symposium on Biomedical Imaging - Paris, France
Duration: 14 May 200817 May 2008

Publication series

NameIEEE International Symposium on Biomedical Imaging
PublisherIEEE
ISSN (Print)1945-7928

Conference

Conference5th IEEE International Symposium on Biomedical Imaging
Country/TerritoryFrance
Period14/05/200817/05/2008

Keywords

  • diffusion weighted MRI
  • constrained spherical deconvolution
  • bootstrap
  • confidence intervals
  • Monte Carlo simulations
  • MRI DATA
  • DT-MRI
  • DIFFUSION
  • BOOTSTRAP

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