Paper Info
Title
A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation.
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.
Year
DOI
Venue
2016
10.1016/j.compmedimag.2016.09.003
Computerized Medical Imaging and Graphics
Keywords
Field
DocType
DTI,Fiber orientation estimation,Volumetric tract segmentation
Computer vision,Diffusion MRI,White matter,Computer science,Segmentation,Imaging phantom,Image noise,Artificial intelligence,Tractography,Bayesian probability,Magnetic resonance imaging
Journal
Volume
ISSN
Citations 
54
0895-6111
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chuyang Ye16111.12
Jerry L. Prince24990488.42