Literature DB >> 26037054

Quantification of voxel-wise total fibre density: Investigating the problems associated with track-count mapping.

Fernando Calamante1, Robert E Smith2, Jacques-Donald Tournier3, David Raffelt2, Alan Connelly4.   

Abstract

A biological parameter that would be valuable to be able to extract from diffusion MRI data is the local white matter axonal density. Track-density imaging (TDI) has been used as if it could provide such a measure; however, this has been the subject of controversy, primarily due to the fact that track-count quantitation is highly sensitive to tracking biases and errors. The spherical-deconvolution informed filtering of tractograms (SIFT) post-processing method was recently introduced to minimise tractography biases, and thus provides a more biologically meaningful measure that could be used in track-count mapping (i.e. TDI following SIFT). The TDI intensity following SIFT ideally corresponds to the orientational average of the fibre orientation distribution (FOD), which corresponds to the total Apparent Fibre Density (AFDtotal) within the AFD framework; in fact, AFDtotal provides a direct measure of local fibre density at native resolution that does not rely on fibre-tracking. In this study, we demonstrate problems associated with quantitative TDI investigations, which can be avoided by using SIFT processing or directly by using AFDtotal maps. We also characterise the intra- and inter-subject reproducibility of TDI maps (with and without SIFT pre-processing) and AFDtotal maps. It is shown that SIFT improves the quantitative characteristics of TDI, but is still vastly inferior to the properties of the AFDtotal parameter itself, because the latter does not require tracking. While standard TDI might be preferable in applications when high anatomical contrast is required, particularly when combined with super-resolution, for voxel-wise quantitation of total tract density (i.e. without tract orientation information) at native resolution, the total AFD maps are preferable to TDI or other related track-count maps. Regardless of the track-count measure, it should be noted that all of these voxel-averaged approaches discard important information that is retained in fibre-specific approaches such as AFD.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diffusion MRI; Fibre-tracking; Quantification; Track density imaging; Track-count; Voxel-based analysis

Mesh:

Year:  2015        PMID: 26037054     DOI: 10.1016/j.neuroimage.2015.05.070

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


  16 in total

Review 1.  Track-weighted imaging methods: extracting information from a streamlines tractogram.

Authors:  Fernando Calamante
Journal:  MAGMA       Date:  2017-02-08       Impact factor: 2.310

2.  Linking spherical mean diffusion weighted signal with intra-axonal volume fraction.

Authors:  Hua Li; Ho Ming Chow; Diane C Chugani; Harry T Chugani
Journal:  Magn Reson Imaging       Date:  2018-11-12       Impact factor: 2.546

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4.  Minimal number of gradient directions for robust measurement of spherical mean diffusion weighted signal.

Authors:  Hua Li; Ho Ming Chow; Diane C Chugani; Harry T Chugani
Journal:  Magn Reson Imaging       Date:  2018-08-30       Impact factor: 2.546

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Authors:  Martin Cousineau; Pierre-Marc Jodoin; Félix C Morency; Verena Rozanski; Marilyn Grand'Maison; Barry J Bedell; Maxime Descoteaux
Journal:  Neuroimage Clin       Date:  2017-07-25       Impact factor: 4.881

8.  Uniformity and Deviation of Intra-axonal Cross-sectional Area Coverage of the Gray-to-White Matter Interface.

Authors:  Stefan Sommer; Sebastian Kozerke; Erich Seifritz; Philipp Staempfli
Journal:  Front Neurosci       Date:  2017-12-22       Impact factor: 4.677

9.  Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves.

Authors:  Emmanuelle Renauld; Maxime Descoteaux; Michaël Bernier; Eleftherios Garyfallidis; Kevin Whittingstall
Journal:  PLoS One       Date:  2016-07-06       Impact factor: 3.240

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Authors:  Jonathan Wirsich; Alistair Perry; Ben Ridley; Timothée Proix; Mathieu Golos; Christian Bénar; Jean-Philippe Ranjeva; Fabrice Bartolomei; Michael Breakspear; Viktor Jirsa; Maxime Guye
Journal:  Neuroimage Clin       Date:  2016-05-19       Impact factor: 4.881

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