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

C. Maffei; G. Girard; K.G. Schilling; D.B. Aydogan; N. Adluru; A. Zhylka; Y. Wu; M. Mancini; A. Hamamci; A. Sarica; A. Teillac; S.H. Baete; D. Karimi; F.C. Yeh; M.E. Yildiz; A. Gholipour; Y. Bihan-Poudec; B. Hiba; A. Quattrone; T. Boshkovski; N. Stikov; P.T. Yap; A. de Luca; J. Pluim; A. Leemans; V. Prabhakaran; B.B. Bendlin; A.L. Alexander; B.A. Landman; E.J. Canales-Rodríguez; M. Barakovic; J. Rafael-Patino; T. Yu; G. Rensonnet; S. Schiavi; A. Daducci; M. Pizzolato; E. Fischi-Gomez; J.P. Thiran; G. Dai; G. Grisot; N. Lazovski; S. Puch; M. Ramos; P. Rodrigues; V. Prčkovska; R. Jones; J. Lehman; S.N. Haber; A. Yendiki

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

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Date

15 août 2022

Type de document

Articles

Périmètre

Publications

Langue

Anglais

Identifiants

doi: 10.1016/j.neuroimage.2022.119327
issn: 1053-8119

Source

Serveur académique Lausannois

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Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2022.119327

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/35636227

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1095-9572

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_F21BD88EA8029

Collection

Serveur Académique Lausannois

Organisation

Université de Lausanne et CHUV

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info:eu-repo/semantics/openAccess , CC BY-NC-ND 4.0 , https://creativecommons.org/licenses/by-nc-nd/4.0/

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Brain/diagnostic imaging; Connectome/methods; Diffusion; Diffusion Magnetic Resonance Imaging/methods; Diffusion Tensor Imaging/methods; Humans; Image Processing, Computer-Assisted/methods; White Matter; Anatomic tracing; Diffusion MRI; Tractography; Validation; White matter anatomy

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Gases--Diffusion Liquids--Diffusion broadcasting

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C. Maffei et al., « Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. », Serveur académique Lausannois, ID : 10.1016/j.neuroimage.2022.119327

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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.

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

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