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

Citation:

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, and Anastasia Yendiki. 2022. “Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.” Neuroimage, 257, Pp. 119327.

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.