Literature DB >> 18816816

Gibbs tracking: a novel approach for the reconstruction of neuronal pathways.

B W Kreher1, I Mader, V G Kiselev.   

Abstract

Reconstruction of neuronal fibers using diffusion-weighted (DW) MRI is an emerging method in biomedical research. Existing fiber-tracking algorithms are commonly based on the "walker principle." Fibers are reconstructed as trajectories of "walkers," which are guided according to local diffusion properties. In this study, a new method of fiber tracking is proposed that does not engage any "walking" algorithm. It resolves a number of inherent problems of the "walking" approach, in particular the reconstruction of crossing and spreading fibers. In the proposed method, the fibers are built with small line elements. Each line element contributes an anisotropic term to the simulated DW signal, which is adjusted to the measured signal. This method demonstrates good results for simulated fibers. A single in vivo result demonstrates the successful reconstruction of the dominant neuronal pathways. A comparison with the diffusion tensor imaging (DTI)-based fiber assignment with continuous tracking (FACT) method and the probabilistic index of connectivity (PICo) method based on a multitensor model is performed for the callosal fibers. The result shows a strong increase in the number of reconstructed fibers. These almost fill the total white matter (WM) volume and connect a large area of the cortex. The method is very computationally expensive. Possible ways to address this problem are discussed. (c) 2008 Wiley-Liss, Inc.

Mesh:

Year:  2008        PMID: 18816816     DOI: 10.1002/mrm.21749

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  42 in total

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2.  A full bi-tensor neural tractography algorithm using the unscented Kalman filter.

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3.  Walk the line: from diffusion imaging to the microstructure of the brain.

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4.  Local and global fiber tractography in patients with epilepsy.

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5.  Global tracking in human gliomas: a comparison with established tracking methods.

Authors:  T Nguyen-Thanh; M Reisert; C Anastasopoulos; F Hamzei; T Reithmeier; M S Vry; V G Kiselev; A Weyerbrock; I Mader
Journal:  Clin Neuroradiol       Date:  2013-01-18       Impact factor: 3.649

Review 6.  Advances in computational and statistical diffusion MRI.

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7.  A generative model for resolution enhancement of diffusion MRI data.

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8.  Electrophysiologic Validation of Diffusion Tensor Imaging Tractography during Deep Brain Stimulation Surgery.

Authors:  V A Coenen; C Jenkner; C R Honey; B Mädler
Journal:  AJNR Am J Neuroradiol       Date:  2016-03-31       Impact factor: 3.825

Review 9.  Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains.

Authors:  Andrew L Alexander; Samuel A Hurley; Alexey A Samsonov; Nagesh Adluru; Ameer Pasha Hosseinbor; Pouria Mossahebi; Do P M Tromp; Elizabeth Zakszewski; Aaron S Field
Journal:  Brain Connect       Date:  2012-01-27

10.  Spatial Mapping of Translational Diffusion Coefficients Using Diffusion Tensor Imaging: A Mathematical Description.

Authors:  Anil N Shetty; Sharon Chiang; Mirjana Maletic-Savatic; Gregor Kasprian; Marina Vannucci; Wesley Lee
Journal:  Concepts Magn Reson Part A Bridg Educ Res       Date:  2014-04-15       Impact factor: 0.481

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