Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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NeuroImage, Volume 257
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Funding Information: Data acquisition was supported by the National Institute of Mental Health (R01-MH045573, P50-MH106435). Additional research support was provided by the National Institute of Biomedical Imaging and Bioengineering (R01-EB021265) and the National Institute of Neurological Disorders and Stroke (R01-NS119911). Imaging was carried out at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41-EB015896, a P41 Biotechnology Resource Grant, and instrumentation supported by the NIH Shared Instrumentation Grant Program (S10RR016811, S10RR023401, S10RR019307, and S10RR023043). Andrey Zhylka is supported by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant (765148). Ye Wu and Pew-Thian Yap were supported in part by the National Institute of Mental Health (R01-MH125479), and the National Institute of Biomedical Imaging and Bioengineering (R01-EB008374). The team at Boston Children's Hospital was supported in part by the National Institutes of Health (NIH) grants R01-NS106030, R01-EB031849, and R01-EB019483. Team from UW-Madison would like to acknowledge the NIH grants R01NS123378, U54HD090256, R01NS092870, R01EB022883, R01AI117924, R01AG027161, RF1AG059312, P50AG033514, R01NS105646, UF1AG051216, R01NS111022, R01NS117568, P01AI132132, R01AI138647, R34DA050258, and R01AG037639. Erick J. Canales-Rodríguez was supported by the Swiss National Science Foundation, Ambizione grant PZ00P2_185814. Matteo Mancini was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. Publisher Copyright: © 2022
Anatomic tracing, Diffusion MRI, Tractography, Validation, White matter anatomy
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Maffei, C, Girard, G, Schilling, K G, Aydogan, D B, Adluru, N, Zhylka, A, Wu, Y, Mancini, M, Hamamci, A, Sarica, A, Teillac, A, Baete, S H, Karimi, D, Yeh, F C, Yildiz, M E, Gholipour, A, Bihan-Poudec, Y, Hiba, B, Quattrone, A, Quattrone, A, Boshkovski, T, Stikov, N, Yap, P T, de Luca, A, Pluim, J, Leemans, A, Prabhakaran, V, Bendlin, B B, Alexander, A L, Landman, B A, Canales-Rodríguez, E J, Barakovic, M, Rafael-Patino, J, Yu, T, Rensonnet, G, Schiavi, S, Daducci, A, Pizzolato, M, Fischi-Gomez, E, Thiran, J P, Dai, G, Grisot, G, Lazovski, N, Puch, S, Ramos, M, Rodrigues, P, Prčkovska, V, Jones, R, Lehman, J & Haber, S N 2022, ' Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI ', NeuroImage, vol. 257, 119327, pp. 1-17 .