MRtrix: Diffusion tractography in crossing fiber regions

By Jacques-Donald Tournier, Fernando Calamante, Alan Connelly. International journal of imaging systems and technology (Print), 2012

1,428 citations recorded by the source corpus.

Subfields: Computational Mathematics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ce926d2e3571f739c5ab44bd723b8d51a46979ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mathematics

1. Jacques-Donald Tournier
2. Maxime Descoteaux
3. Alan Connelly
4. Rowan J. Smith
5. Alexander Leemans
6. Ben Jeurissen
7. Fernando Calamante
8. Susumu Mori

In Radiology, Nuclear Medicine and Imaging

1. Jacques-Donald Tournier
2. Ben Jeurissen
3. Alan Connelly
4. Alexander Leemans
5. Maxime Descoteaux
6. Jan Sijbers
7. Fernando Calamante
8. Rowan J. Smith

Citations in the corpus

• Diffusion MRI fiber tractography of the brain (2019, methodology): “A notable example of this approach is MRtrix's probabilistic fODF tracking.(36) In a sense, this approach can be regarded as providing more biophysically meaningful tractograms, as it is driven by anatomical dispersion rather than noise characteristics and model inaccuracies.”
• Towards quantitative connectivity analysis: reducing tractography biases (2014, positive): “It 55 can either be the tracking mask (Centuro et al., 1999; Huang et al., 2004; Tournier et al., 2012, 2011), a WM/GM 56 interface mask (Li et al., 2012b; Smith et al., 2012) or a region of interest (Behrens et al., 2007; Huang et al., 2004; 57 Parker and Alexander, 2005; Toosy et al., 2004;…”
• Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, methodology): “For all processing steps where the relevant software is not explicitly stated, processingwas performed using MRtrix (Tournier et al., 2012) or in-house modifications thereof.”
• Denoising of diffusion MRI using random matrix theory (2016, methodology): “Finally, probabilistic streamline fiber tractography was performed to investigate the effect of denoising on the tractogram using the MRtrix software package (Jeurissen et al., 2013; Tournier et al., 2012).”
• SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI (2019, neutral): “The dataset was denoised using MRtrix3 (Tournier et al., 2012) (http://www.mrtrix.org) and corrected for Gibbs ringing (Kellner et al., 2016).”
• Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, neutral): “12 shows a sagittal slab visualisation of a whole brain probabilistic tractogram of data set 3 calculated from both SSST- and MSMT-CSD fODFs using both an fODF amplitude threshold of 0.1 (default) and 0.05 (half of the default) (Tournier et al., 2012).”
• Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres (2015, methodology): “We note that thresholding fi xels based on the AFD may undesirably exclude other fi xels in crossing fi bre regions that have low AFD (due to partial volume effects).”
• Network‐based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions (2021, methodology): “…were analyzed using FSL and MRtrix (http://mrtrix.org) software packages.Dataweredenoised, correctedwith eddy current correction, and bias-field corrected using MRtrix (Tournier et al., 2012) and FSL. Constrained spherical deconvolution was used in calculating the fiber orientation distribution.”