Literature DB >> 16249099

Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging.

P Staempfli1, T Jaermann, G R Crelier, S Kollias, A Valavanis, P Boesiger.   

Abstract

Magnetic resonance diffusion tensor tractography is a powerful tool for the non-invasive depiction of the white matter architecture in the human brain. However, due to limitations in the underlying tensor model, the technique is often unable to reconstruct correct trajectories in heterogeneous fiber arrangements, such as axonal crossings. A novel tractography method based on fast marching (FM) is proposed which is capable of resolving fiber crossings and also permits trajectories to branch. It detects heterogeneous fiber arrangements by incorporating information from the entire diffusion tensor. The FM speed function is adapted to the local tensor characteristics, allowing in particular to maintain the front evolution direction in crossing situations. In addition, the FM's discretization error is reduced by increasing the number of considered possible front evolution directions. The performance of the technique is demonstrated in artificial data and in the healthy human brain. Comparisons with standard FM tractography and conventional line propagation algorithms show that, in the presence of interfering structures, the proposed method is more accurate in reconstructing trajectories. The in vivo results illustrate that the elucidated major white matter pathways are consistent with known anatomy and that multiple crossings and tract branching are handled correctly.

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Year:  2005        PMID: 16249099     DOI: 10.1016/j.neuroimage.2005.09.027

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


  24 in total

Review 1.  Principles and limitations of computational algorithms in clinical diffusion tensor MR tractography.

Authors:  H-W Chung; M-C Chou; C-Y Chen
Journal:  AJNR Am J Neuroradiol       Date:  2010-03-18       Impact factor: 3.825

2.  DTI at long diffusion time improves fiber tracking.

Authors:  Swati Rane; Govind Nair; Timothy Q Duong
Journal:  NMR Biomed       Date:  2010-06       Impact factor: 4.044

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

Authors:  Hai Li; Zhong Xue; Kemi Cui; Stephen T C Wong
Journal:  Comput Med Imaging Graph       Date:  2010-10-28       Impact factor: 4.790

4.  Cortical projections to the human red nucleus: a diffusion tensor tractography study with a 1.5-T MRI machine.

Authors:  Christophe Habas; Emmanuel Alain Cabanis
Journal:  Neuroradiology       Date:  2006-08-26       Impact factor: 2.804

5.  A preliminary study of the effects of trigger timing on diffusion tensor imaging of the human spinal cord.

Authors:  P Summers; P Staempfli; T Jaermann; S Kwiecinski; S Kollias
Journal:  AJNR Am J Neuroradiol       Date:  2006-10       Impact factor: 3.825

6.  Multitensor tractography enables better depiction of motor pathways: initial clinical experience using diffusion-weighted MR imaging with standard b-value.

Authors:  K Yamada; K Sakai; F G C Hoogenraad; R Holthuizen; K Akazawa; H Ito; H Oouchi; S Matsushima; T Kubota; H Sasajima; K Mineura; T Nishimura
Journal:  AJNR Am J Neuroradiol       Date:  2007-09-20       Impact factor: 3.825

7.  Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling.

Authors:  Fan Zhang; Edwin R Hancock; Casey Goodlett; Guido Gerig
Journal:  Med Image Anal       Date:  2008-06-05       Impact factor: 8.545

8.  Fast approximate stochastic tractography.

Authors:  Juan Eugenio Iglesias; Paul M Thompson; Cheng-Yi Liu; Zhuowen Tu
Journal:  Neuroinformatics       Date:  2012-01

9.  Performing real-time interactive fiber tracking.

Authors:  Adiel Mittmann; Tiago H C Nobrega; Eros Comunello; Juliano P O Pinto; Paulo R Dellani; Peter Stoeter; Aldo von Wangenheim
Journal:  J Digit Imaging       Date:  2010-02-13       Impact factor: 4.056

10.  Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU.

Authors:  Andac Hamamci
Journal:  Neuroinformatics       Date:  2020-01
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