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Medical-image Analysis and Statistical Interpretation Lab

Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions

Posted by on Saturday, December 15, 2018 in Crossing Fibers, Diffusion Tensor Imaging, Diffusion Weighted MRI, Neuroimaging, News, Reproducibility, Tractography.

Kurt G Schilling, Alessandro Daducci, Klaus Maier-Hein, Cyril Poupon, Jean-Christophe Houde, Vishwesh Nath, Adam W Anderson, Bennett A Landman, Maxime Descoteaux. “Challenges in Diffusion MRI Tractography – Lessons Learned from International Benchmark Competitions”. Magnetic Resonance Imaging. 2018. doi: 10.1016/j.mri.2018.11.014

Full text: NIHMSID

https://www.sciencedirect.com/science/article/pii/S0730725X18305162#f0005

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:

Diffusion MRI; Challenges; Tractography; Validation; Algorithms; Accuracy
Fig. 1. Past challenges in fiber tractography. Detailed description of data, ground truth, and evaluation are described in the text. (A) FiberCup Phantom pathways with 16 ground truth bundles [21]. (B) Eight example CST reconstructions from the DTI Challenge [23]. (C) Synthetic fiber fields from the HARDI Reconstruction Challenge [26]. (D) Phantomas [27] dataset for Tractometer evaluation [28]. (E) Creation of simulated in vivo human dataset for the ISMRM Tractography Challenge [29]. (F) Example submissions from the TraCED Reproducibility Challenge for two white matter pathways. (G) 3D-VoTEM ground truths defined on the macaque, squirrel monkey, and phantom (from left to right).
Fig. 1. Past challenges in fiber tractography. Detailed description of data, ground truth, and evaluation are described in the text. (A) FiberCup Phantom pathways with 16 ground truth bundles . (B) Eight example CST reconstructions from the DTI Challenge . (C) Synthetic fiber fields from the HARDI Reconstruction Challenge . (D) Phantomas  dataset for Tractometer evaluation . (E) Creation of simulated in vivo human dataset for the ISMRM Tractography Challenge . (F) Example submissions from the TraCED Reproducibility Challenge for two white matter pathways. (G) 3D-VoTEM ground truths defined on the macaque, squirrel monkey, and phantom (from left to right).