Browsing by Author "Bendlin, Barbara B."

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    Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI
    (ACADEMIC PRESS, 2022-08-15) Maffei, Chiara; Girard, Gabriel; Schilling, Kurt G.; Aydogan, Dogu Baran; Adluru, Nagesh; Zhylka, Andrey; Wu, Ye; Mancini, Matteo; Hamamci, Andac; Sarica, Alessia; Teillac, Achille; Baete, Steven H.; Karimi, Davood; Yeh, Fang Cheng; Yildiz, Mert E.; Gholipour, Ali; Bihan-Poudec, Yann; Hiba, Bassem; Quattrone, Andrea; Quattrone, Aldo; Boshkovski, Tommy; Stikov, Nikola; Yap, Pew Thian; de Luca, Alberto; Pluim, Josien; Leemans, Alexander; Prabhakaran, Vivek; Bendlin, Barbara B.; Alexander, Andrew L.; Landman, Bennett A.; Canales-Rodríguez, Erick J.; Barakovic, Muhamed; Rafael-Patino, Jonathan; Yu, Thomas; Rensonnet, Gaëtan; Schiavi, Simona; Daducci, Alessandro; Pizzolato, Marco; Fischi-Gomez, Elda; Thiran, Jean Philippe; Dai, George; Grisot, Giorgia; Lazovski, Nikola; Puch, Santi; Ramos, Marc; Rodrigues, Paulo; Prčkovska, Vesna; Jones, Robert; Lehman, Julia; Haber, Suzanne N.; Department of Neuroscience and Biomedical Engineering; Massachusetts General Hospital; University of Lausanne; Vanderbilt University; University of Wisconsin-Madison; Eindhoven University of Technology; University of North Carolina at Chapel Hill; Cardiff University; Yeditepe University; University Magna Græcia; Centre National de la Recherche Scientifique (CNRS); New York University; Harvard Medical School; University of Pittsburgh; Polytechnique Montreal; Utrecht University; Swiss Federal Institute of Technology Lausanne; University of Basel; CIBM Center for BioMedical Imaging; University of Verona; Wellesley College; DeepHealth, Inc.; QMENTA Inc.; University of Rochester
    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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    Tractography passes the test : Results from the diffusion-simulated connectivity (disco) challenge
    (Academic Press, 2023-08-15) Girard, Gabriel; Rafael-Patiño, Jonathan; Truffet, Raphaël; Aydogan, Dogu Baran; Adluru, Nagesh; Nair, Veena A.; Prabhakaran, Vivek; Bendlin, Barbara B.; Alexander, Andrew L.; Bosticardo, Sara; Gabusi, Ilaria; Ocampo-Pineda, Mario; Battocchio, Matteo; Piskorova, Zuzana; Bontempi, Pietro; Schiavi, Simona; Daducci, Alessandro; Stafiej, Aleksandra; Ciupek, Dominika; Bogusz, Fabian; Pieciak, Tomasz; Frigo, Matteo; Sedlar, Sara; Deslauriers-Gauthier, Samuel; Kojčić, Ivana; Zucchelli, Mauro; Laghrissi, Hiba; Ji, Yang; Deriche, Rachid; Schilling, Kurt G.; Landman, Bennett A.; Cacciola, Alberto; Basile, Gianpaolo Antonio; Bertino, Salvatore; Newlin, Nancy; Kanakaraj, Praitayini; Rheault, Francois; Filipiak, Patryk; Shepherd, Timothy M.; Lin, Ying Chia; Placantonakis, Dimitris G.; Boada, Fernando E.; Baete, Steven H.; Hernández-Gutiérrez, Erick; Ramírez-Manzanares, Alonso; Coronado-Leija, Ricardo; Stack-Sánchez, Pablo; Concha, Luis; Descoteaux, Maxime; Mansour L．, Sina; Seguin, Caio; Zalesky, Andrew; Marshall, Kenji; Canales-Rodríguez, Erick J.; Wu, Ye; Ahmad, Sahar; Yap, Pew Thian; Théberge, Antoine; Gagnon, Florence; Massi, Frédéric; Fischi-Gomez, Elda; Gardier, Rémy; Haro, Juan Luis Villarreal; Pizzolato, Marco; Caruyer, Emmanuel; Thiran, Jean Philippe; Department of Neuroscience and Biomedical Engineering; University of Lausanne; Centre National de la Recherche Scientifique (CNRS); University of Wisconsin-Madison; University of Verona; University of Genoa; AGH University of Science and Technology; Sano Centre for Computational Personalised Medicine; Université Côte d'Azur; Vanderbilt University; University of Messina; New York University; Stanford University; Université de Sherbrooke; Consejo Nacional de Ciencia y Tecnologia Mexico; Universidad Nacional Autónoma de México; University of Melbourne; Swiss Federal Institute of Technology Lausanne; University of North Carolina at Chapel Hill
    Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.