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

Chiara Maffei; Gabriel Girard; Kurt G. Schilling; Dogu Baran Aydogan; Nagesh Adluru; Andrey Zhylka; Ye Wu; Matteo Mancini; Andac Hamamci; Alessia Sarica; Achille Teillac; Steven H. Baete; Davood Karimi; Fang Cheng Yeh; Mert E. Yildiz; Ali Gholipour; Yann Bihan-Poudec; Bassem Hiba; Andrea Quattrone; Aldo Quattrone; Tommy Boshkovski; Nikola Stikov; Pew Thian Yap; Alberto de Luca; Josien Pluim; Alexander Leemans; Vivek Prabhakaran; Barbara B. Bendlin; Andrew L. Alexander; Bennett A. Landman; Erick J. Canales-Rodríguez; Muhamed Barakovic; Jonathan Rafael-Patino; Thomas Yu; Gaëtan Rensonnet; Simona Schiavi; Alessandro Daducci; Marco Pizzolato; Elda Fischi-Gomez; Jean Philippe Thiran; George Dai; Giorgia Grisot; Nikola Lazovski; Santi Puch; Marc Ramos; Paulo Rodrigues; Vesna Prčkovska; Robert Jones; Julia Lehman; Suzanne N. Haber; Anastasia Yendiki

doi:10.1016/j.neuroimage.2022.119327

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

Chiara Maffei (Corresponding author), Gabriel Girard, Kurt G. Schilling, Dogu Baran Aydogan, Nagesh Adluru, Andrey Zhylka, Ye Wu, Matteo Mancini, Andac Hamamci, Alessia Sarica, Achille Teillac, Steven H. Baete, Davood Karimi, Fang Cheng Yeh, Mert E. Yildiz, Ali Gholipour, Yann Bihan-Poudec, Bassem Hiba, Andrea Quattrone, Aldo QuattroneTommy Boshkovski, Nikola Stikov, Pew Thian Yap, Alberto de Luca, Josien Pluim, Alexander Leemans, Vivek Prabhakaran, Barbara B. Bendlin, Andrew L. Alexander, Bennett A. Landman, Erick J. Canales-Rodríguez, Muhamed Barakovic, Jonathan Rafael-Patino, Thomas Yu, Gaëtan Rensonnet, Simona Schiavi, Alessandro Daducci, Marco Pizzolato, Elda Fischi-Gomez, Jean Philippe Thiran, George Dai, Giorgia Grisot, Nikola Lazovski, Santi Puch, Marc Ramos, Paulo Rodrigues, Vesna Prčkovska, Robert Jones, Julia Lehman, Suzanne N. Haber, Anastasia Yendiki

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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.

Originele taal-2	Engels
Artikelnummer	119327
Aantal pagina's	17
Tijdschrift	Neuroimage
Volume	257
DOI's	https://doi.org/10.1016/j.neuroimage.2022.119327
Status	Gepubliceerd - 15 aug. 2022

Bibliografische nota

Publisher Copyright:
© 2022

Financiering

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].

Financiers	Financiernummer
Center for Functional Neuroimaging Technologies	P41-EB015896, S10RR016811
National Institutes of Health, NIH	R01NS105646
National Institute of Mental Health	R01MH125479, P50MH106435, R01MH045573
National Institute on Drug Abuse	R34DA050258
National Institute on Aging	R01AG037639, RF1AG059312, R01AG027161, P30AG062715, P50AG033514, UF1AG051216
National Institute of Allergy and Infectious Diseases	P01AI132132, R01AI138647, U54AI117924
National Institute of Neurological Disorders and Stroke	R01NS111022, R01NS117568, R01NS119911, R01NS092870, R01NS120954, K23NS096056, R01NS106030, R01NS123378
National Institute of Biomedical Imaging and Bioengineering	R01EB028774, R01EB031849, R01EB019483, P41EB015896, U01EB026996, R01EB008374, R01EB021265, R01EB022883
National Institute of Child Health and Human Development	U54HD090256
National Center for Research Resources	S10RR023043, S10RR019307, S10RR023401
Massachusetts General Hospital
Wellcome Trust	213722/Z/18/Z
H2020 Marie Skłodowska-Curie Actions	765148, R01-MH125479, R01-EB008374
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung	PZ00P2_185814
Horizon 2020

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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., ... Yendiki, A. (2022). Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage, 257, Artikel 119327. https://doi.org/10.1016/j.neuroimage.2022.119327

@article{8bb59b113e60472d92400465621604e6,

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

abstract = "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.",

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

author = "Chiara Maffei and Gabriel Girard and Schilling, {Kurt G.} and Aydogan, {Dogu Baran} and Nagesh Adluru and Andrey Zhylka and Ye Wu and Matteo Mancini and Andac Hamamci and Alessia Sarica and Achille Teillac and Baete, {Steven H.} and Davood Karimi and Yeh, {Fang Cheng} and Yildiz, {Mert E.} and Ali Gholipour and Yann Bihan-Poudec and Bassem Hiba and Andrea Quattrone and Aldo Quattrone and Tommy Boshkovski and Nikola Stikov and Yap, {Pew Thian} and {de Luca}, Alberto and Josien Pluim and Alexander Leemans and Vivek Prabhakaran and Bendlin, {Barbara B.} and Alexander, {Andrew L.} and Landman, {Bennett A.} and Canales-Rodr{\'i}guez, {Erick J.} and Muhamed Barakovic and Jonathan Rafael-Patino and Thomas Yu and Ga{\"e}tan Rensonnet and Simona Schiavi and Alessandro Daducci and Marco Pizzolato and Elda Fischi-Gomez and Thiran, {Jean Philippe} and George Dai and Giorgia Grisot and Nikola Lazovski and Santi Puch and Marc Ramos and Paulo Rodrigues and Vesna Pr{\v c}kovska and Robert Jones and Julia Lehman and Haber, {Suzanne N.} and Anastasia Yendiki",

note = "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-Rodr{\'i}guez 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]. ",

year = "2022",

month = aug,

day = "15",

doi = "10.1016/j.neuroimage.2022.119327",

language = "English",

volume = "257",

journal = "Neuroimage",

issn = "1053-8119",

publisher = "Academic Press Inc.",

}

Maffei, C, Girard, G, Schilling, KG, Aydogan, DB, Adluru, N, Zhylka, A, Wu, Y, Mancini, M, Hamamci, A, Sarica, A, Teillac, A, Baete, SH, Karimi, D, Yeh, FC, Yildiz, ME, Gholipour, A, Bihan-Poudec, Y, Hiba, B, Quattrone, A, Quattrone, A, Boshkovski, T, Stikov, N, Yap, PT, de Luca, A, Pluim, J, Leemans, A, Prabhakaran, V, Bendlin, BB, Alexander, AL, Landman, BA, Canales-Rodríguez, EJ, Barakovic, M, Rafael-Patino, J, Yu, T, Rensonnet, G, Schiavi, S, Daducci, A, Pizzolato, M, Fischi-Gomez, E, Thiran, JP, Dai, G, Grisot, G, Lazovski, N, Puch, S, Ramos, M, Rodrigues, P, Prčkovska, V, Jones, R, Lehman, J, Haber, SN & Yendiki, A 2022, 'Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI', Neuroimage, vol. 257, 119327. https://doi.org/10.1016/j.neuroimage.2022.119327

TY - JOUR

T1 - Insights from the IronTract challenge

T2 - Optimal methods for mapping brain pathways from multi-shell diffusion MRI

AU - Maffei, Chiara

AU - Girard, Gabriel

AU - Schilling, Kurt G.

AU - Aydogan, Dogu Baran

AU - Adluru, Nagesh

AU - Zhylka, Andrey

AU - Wu, Ye

AU - Mancini, Matteo

AU - Hamamci, Andac

AU - Sarica, Alessia

AU - Teillac, Achille

AU - Baete, Steven H.

AU - Karimi, Davood

AU - Yeh, Fang Cheng

AU - Yildiz, Mert E.

AU - Gholipour, Ali

AU - Bihan-Poudec, Yann

AU - Hiba, Bassem

AU - Quattrone, Andrea

AU - Quattrone, Aldo

AU - Boshkovski, Tommy

AU - Stikov, Nikola

AU - Yap, Pew Thian

AU - de Luca, Alberto

AU - Pluim, Josien

AU - Leemans, Alexander

AU - Prabhakaran, Vivek

AU - Bendlin, Barbara B.

AU - Alexander, Andrew L.

AU - Landman, Bennett A.

AU - Canales-Rodríguez, Erick J.

AU - Barakovic, Muhamed

AU - Rafael-Patino, Jonathan

AU - Yu, Thomas

AU - Rensonnet, Gaëtan

AU - Schiavi, Simona

AU - Daducci, Alessandro

AU - Pizzolato, Marco

AU - Fischi-Gomez, Elda

AU - Thiran, Jean Philippe

AU - Dai, George

AU - Grisot, Giorgia

AU - Lazovski, Nikola

AU - Puch, Santi

AU - Ramos, Marc

AU - Rodrigues, Paulo

AU - Prčkovska, Vesna

AU - Jones, Robert

AU - Lehman, Julia

AU - Haber, Suzanne N.

AU - Yendiki, Anastasia

N1 - 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].

PY - 2022/8/15

Y1 - 2022/8/15

N2 - 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.

AB - 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.

KW - Anatomic tracing

KW - Diffusion MRI

KW - Tractography

KW - Validation

KW - White matter anatomy

KW - White Matter

KW - Humans

KW - Connectome/methods

KW - Brain/diagnostic imaging

KW - Image Processing, Computer-Assisted/methods

KW - Diffusion Magnetic Resonance Imaging/methods

KW - Diffusion

KW - Diffusion Tensor Imaging/methods

UR - http://www.scopus.com/inward/record.url?scp=85131443264&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2022.119327

DO - 10.1016/j.neuroimage.2022.119327

M3 - Article

C2 - 35636227

AN - SCOPUS:85131443264

SN - 1053-8119

VL - 257

JO - Neuroimage

JF - Neuroimage

M1 - 119327

ER -

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

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