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Magn Reson Imaging. 2019 Apr;57:194-209. doi: 10.1016/j.mri.2018.11.014. Epub 2018 Nov 29.

Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions.

Author information

1
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America. Electronic address: kurt.g.schilling.1@vumc.org.
2
Computer Science Department, University of Verona, Verona, Italy.
3
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
4
Neurospin, Frédéric Joliot Life Sciences Institute, CEA, Gif-sur-Yvette, France.
5
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Québec, Canada.
6
Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States of America.
7
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America.
8
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America.

Abstract

Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging community due to its ability to noninvasively map the structural connectivity of the brain. Despite widespread use in clinical and research domains, these methods suffer from several potential drawbacks or limitations. Thus, validating the accuracy and reproducibility of techniques is critical for sound scientific conclusions and effective clinical outcomes. Towards this end, a number of international benchmark competitions, or "challenges", has been organized by the diffusion MRI community in order to investigate the reliability of the tractography process by providing a platform to compare algorithms and results in a fair manner, and evaluate common and emerging algorithms in an effort to advance the state of the field. In this paper, we summarize the lessons from a decade of challenges in tractography, and give perspective on the past, present, and future "challenges" that the field of diffusion tractography faces.

KEYWORDS:

Accuracy; Algorithms; Challenges; Diffusion MRI; Tractography; Validation

PMID:
30503948
PMCID:
PMC6331218
[Available on 2020-04-01]
DOI:
10.1016/j.mri.2018.11.014
[Indexed for MEDLINE]

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