Dipy, a library for the analysis of diffusion MRI data
Eleftherios Garyfallidis,Eleftherios Garyfallidis,Matthew Brett,Bagrat Amirbekian,Ariel Rokem,Stefan van der Walt,Maxime Descoteaux,Ian Nimmo-Smith +7 more
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TLDR
Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface, and has implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography.Abstract:
Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.read more
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MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Jacques-Donald Tournier,Robert E. Smith,Robert E. Smith,David Raffelt,Rami Tabbara,Thijs Dhollander,Thijs Dhollander,Maximilian Pietsch,Daan Christiaens,Ben Jeurissen,Chun-Hung Yeh,Chun-Hung Yeh,Alan Connelly,Alan Connelly +13 more
TL;DR: A high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software is provided.
Journal ArticleDOI
The challenge of mapping the human connectome based on diffusion tractography
Klaus H. Maier-Hein,Peter F. Neher,Jean-Christophe Houde,Marc-Alexandre Côté,Eleftherios Garyfallidis,Jidan Zhong,Maxime Chamberland,Fang-Cheng Yeh,Ying-Chia Lin,Qing Ji,Wilburn E. Reddick,John O. Glass,David Qixiang Chen,Yuanjing Feng,Chengfeng Gao,Ye Wu,Jieyan Ma,H Renjie,Qiang Li,Carl-Fredrik Westin,Samuel Deslauriers-Gauthier,J. Omar Ocegueda Gonzalez,Michael Paquette,Samuel St-Jean,Gabriel Girard,François Rheault,Jasmeen Sidhu,Chantal M. W. Tax,Fenghua Guo,Hamed Y. Mesri,Szabolcs David,Martijn Froeling,Anneriet M. Heemskerk,Alexander Leemans,Arnaud Boré,Basile Pinsard,Christophe Bedetti,Matthieu Desrosiers,Simona M. Brambati,Julien Doyon,Alessia Sarica,Roberta Vasta,Antonio Cerasa,Aldo Quattrone,Jason D. Yeatman,Ali R. Khan,Wes Hodges,Simon Alexander,David Romascano,Muhamed Barakovic,Anna Auría,Oscar Esteban,Alia Lemkaddem,Jean-Philippe Thiran,Hasan Ertan Cetingul,Benjamin L. Odry,Boris Mailhe,Mariappan S. Nadar,Fabrizio Pizzagalli,Gautam Prasad,Julio E. Villalon-Reina,Justin Galvis,Paul M. Thompson,Francisco De Santiago Requejo,Pedro Luque Laguna,Luis Miguel Lacerda,Rachel Barrett,Flavio Dell'Acqua,Marco Catani,Laurent Petit,Emmanuel Caruyer,Alessandro Daducci,Tim B. Dyrby,Tim Holland-Letz,Claus C. Hilgetag,Bram Stieltjes,Maxime Descoteaux +76 more
TL;DR: The encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent) is reported, however, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups.
Journal ArticleDOI
Harmonization of multi-site diffusion tensor imaging data.
Jean-Philippe Fortin,Drew Parker,Birkan Tunç,Takanori Watanabe,Mark A. Elliott,Kosha Ruparel,David R. Roalf,Theodore D. Satterthwaite,Ruben C. Gur,Raquel E. Gur,Robert T. Schultz,Ragini Verma,Russell T. Shinohara +12 more
TL;DR: It is shown that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses, and that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps.
Journal ArticleDOI
TractSeg - Fast and accurate white matter tract segmentation
TL;DR: TractSeg is a novel convolutional neural network‐based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation, and is demonstrated to be much faster than existing methods while providing unprecedented accuracy.
Journal ArticleDOI
Towards quantitative connectivity analysis: reducing tractography biases.
TL;DR: It is shown that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution, a critical step towards producing tractography results for quantitative structural connectivity analysis.
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