A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation

Chuyang Ye, Jerry Ladd Prince

Research output: Contribution to journalArticle

Abstract

Diffusion magnetic resonance imaging (dMRI) provides information about the microstructure of white matter in the human brain. From dMRI, streamlining tractography is often used to reconstruct computational representations of white matter tracts from which differences in structural connectivity can be explored. In the fiber tracking process, anatomical information can help reduce tracking errors caused by crossing fibers and image noise. In this paper, we propose a Bayesian method for estimating fiber orientations (FOs) guided by anatomical tract information using diffusion tensor imaging (DTI), which is a standard clinical and research dMRI protocol. The proposed method is named Fiber Orientation Reconstruction guided by Tract Segmentation (FORTS). A first step segments and labels the white matter tracts volumetrically, including explicit representations of crossing regions. A second step estimates the FOs using the diffusion information and the anatomical knowledge from segmented white matter tracts. A single FO is estimated in the noncrossing regions while two FOs are estimated in the crossing regions. A third step carries out streamlining tractography that uses information from both the segmented tracts and the estimated FOs. Experiments performed on a digital crossing phantom, a physical phantom, and brain DTI of 18 healthy subjects show that FORTS is able to use the anatomical information to produce FOs with better accuracy and to reduce anatomically incorrect streamlines. In particular, on the brain DTI data, we studied the connectivity of anatomically defined tracts to cortical areas, which is not straightforwardly achievable using only volumetric tract segmentation. These connectivity results demonstrate the potential application of FORTS to scientific studies.

Original languageEnglish (US)
Pages (from-to)35-47
Number of pages13
JournalComputerized Medical Imaging and Graphics
Volume54
DOIs
StatePublished - Dec 1 2016

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Bayes Theorem
Fiber reinforced materials
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Diffusion tensor imaging
Brain
Magnetic resonance
Imaging techniques
Healthy Volunteers
White Matter
Fibers
Information use
Research
Labels
Microstructure

Keywords

  • DTI
  • Fiber orientation estimation
  • Volumetric tract segmentation

ASJC Scopus subject areas

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

Cite this

A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation. / Ye, Chuyang; Prince, Jerry Ladd.

In: Computerized Medical Imaging and Graphics, Vol. 54, 01.12.2016, p. 35-47.

Research output: Contribution to journalArticle

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