Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom

Abstract

As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to fibercup09@gmail.com and updates will be published on the webpage.

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

“…To test the Bingham fit and to estimate how it behaves in more intricate geometries and under more realistic conditions, while still having ground truth knowledge to validate the results, we used the phantom data from the Fiber Cup contest (Fillard et al, 2011). The creation of the physical phantom is described in the work of Poupon et al (2008).…”

“…The same normalization was applied to the phantom data using the fiber density of 1900 fibers per mm³, as described by Fillard et al (2011).…”

DiffusionKit: A light one-stop solution for diffusion MRI data analysis

“…The dataset with 2000 s/mm 2 was from the "Fiber Cup", the intention of which is to evaluate the performance of different modeling and tracking approaches (Fillard et al, 2011). Figs.…”

Fiber up‐sampling and quality assessment of tractograms – towards quantitative brain connectivity

“…The performance of tracking algorithms has significantly improved by considering the information contained in orientation distribution functions (ODF) or fiber orientation distribution (FOD), especially in regions with complex fiber configurations (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007; Fillard et al., 2011; Tournier, Mori, & Leemans, 2011). However, tractograms remain biased by algorithmic‐specific parameters, that is, stopping criteria, curvature thresholds, seed point distribution, and the choice of the tracking algorithm itself, as well as partial volume effects of different fiber populations or various tissue types within the acquired data voxels.…”