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Eleftherios Garyfallidis

Researcher at Indiana University

Publications -  80
Citations -  4063

Eleftherios Garyfallidis is an academic researcher from Indiana University. The author has contributed to research in topics: Diffusion MRI & Computer science. The author has an hindex of 18, co-authored 64 publications receiving 2785 citations. Previous affiliations of Eleftherios Garyfallidis include Cognition and Brain Sciences Unit & University of Cambridge.

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The challenge of mapping the human connectome based on diffusion tractography

Klaus H. Maier-Hein, +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.
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Dipy, a library for the analysis of diffusion MRI data

TL;DR: 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.
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QuickBundles, a Method for Tractography Simplification

TL;DR: A simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds and can help in the search for similarities across several subjects.
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Tractometer: towards validation of tractography pipelines.

TL;DR: Overall, it is shown that averaging improves quality of tractography, sharp angular ODF profiles helps tractography and deterministic tractography produces less invalid tracts which leads to better connectivity results than probabilistic tractography.
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Recognition of white matter bundles using local and global streamline-based registration and clustering.

TL;DR: The purpose of the proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform and robust and adaptive to incomplete data and bundles with missing components.