Diffusion tensor-based fast marching for modeling human brain connectivity network

Hai Li, Zhong Xue, Kemi Cui, Stephen T.C. Wong

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Diffusion tensor imaging (DTI) is an effective modality in studying the connectivity of the brain. To eliminate possible biases caused by fiber extraction approaches due to low spatial resolution of DTI and the number of fibers obtained, the fast marching (FM) algorithm based on the whole diffusion tensor information is proposed to model and study the brain connectivity network. Our observation is that the connectivity extracted from the whole tensor field would be more robust and reliable for constructing brain connectivity network using DTI data. To construct the connectivity network, in this paper, the arrival time map and the velocity map generated by the FM algorithm are combined to define the connectivity strength among different brain regions. The conventional fiber tracking-based and the proposed tensor-based FM connectivity methods are compared, and the results indicate that the connectivity features obtained using the FM-based method agree better with the neuromorphical studies of the human brain.

Original languageEnglish (US)
Pages (from-to)167-178
Number of pages12
JournalComputerized Medical Imaging and Graphics
Volume35
Issue number3
DOIs
StatePublished - Apr 2011

Keywords

  • Brain connectivity analysis
  • Diffusion tensor imaging
  • Fast marching
  • Fiber tracking
  • Tractography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Diffusion tensor-based fast marching for modeling human brain connectivity network'. Together they form a unique fingerprint.

Cite this