Literature DB >> 20570617

Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy.

Yajing Zhang1, Jiangyang Zhang, Kenichi Oishi, Andreia V Faria, Hangyi Jiang, Xin Li, Kazi Akhter, Pedro Rosa-Neto, G Bruce Pike, Alan Evans, Arthur W Toga, Roger Woods, John C Mazziotta, Michael I Miller, Peter C M van Zijl, Susumu Mori.   

Abstract

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification. Copyright 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20570617      PMCID: PMC2910162          DOI: 10.1016/j.neuroimage.2010.05.049

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  36 in total

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  133 in total

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