A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography

Iman Aganj, Christophe Lenglet, Neda Jahanshad, Essa Yacoub, Noam Harel, Paul M. Thompson, Guillermo Sapiro

Research output: Contribution to journalArticlepeer-review

107 Scopus citations

Abstract

A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets.

Original languageEnglish (US)
Pages (from-to)414-425
Number of pages12
JournalMedical Image Analysis
Volume15
Issue number4
DOIs
StatePublished - Aug 2011

Keywords

  • Diffusion-weighted magnetic resonance imaging (DWI)
  • Hough transform
  • Orientation distribution function (ODF)
  • Population studies
  • Tractography

Fingerprint Dive into the research topics of 'A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography'. Together they form a unique fingerprint.

Cite this