A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation----Institute of Automation

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

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Title:

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

Authors: Ye, CY; Prince, JL

Author Full Names: Ye, Chuyang; Prince, Jerry L.

Source:

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 54 35-47; 10.1016/j.compmedimag.2016.09.003

DEC 2016

Language: English

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. (C) 2016 Elsevier Ltd. All rights reserved.

ISSN: 0895-6111

eISSN: 1879-0771

IDS Number:

EF7MJ

Unique ID: WOS:000390513400005

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