Abstract #1728

On the generalizability of diffusion MRI signal representations across acquisition parameters: chronicles of the MEMENTO challenge.

Alberto De Luca^1,2, Andrada Ianus^3,4, Alexander Leemans², Marco Palombo⁵, Hui G Zhang⁵, Daniel C Alexander⁵, Markus Nilsson⁶, Geert-Jan Biessels¹, Mauro Zucchelli⁷, Matteo Frigo⁷, Enes Albay^7,8, Sara Sedlar⁷, Abib Alimi⁷, Samuel Deslauriers-Gauthier⁷, Rachid Deriche⁷, Rutger Fick⁹, Maryam Afzali¹⁰, Tomasz Pieciak^11,12, Fabian Bogusz¹¹, Santiago Aja-Fernandez¹², Evren Özarslan^13,14, Derek K Jones¹⁰, Haoze Chen¹⁵, Mingwu Jin¹⁶, Zhijie Zhang¹⁵, Fengxiang Wang¹⁵, Vishwesh Nath¹⁷, Prasanna Parvathaneni¹⁸, Jan Morez¹⁹, Jan Sijbers¹⁹, Ben Jeurissen¹⁹, Shreyas Fadnavis²⁰, Stefan Endres²¹, Ariel Rokem²², Eleftherios Garyfallidis²⁰, Irina Sanchez²³, Vesna Prchkovska²³, Paulo Rodrigues²³, Bennett A Landman²⁴, and Kurt G Schilling²⁴

¹Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands, ²PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ³Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ⁴Centre for Medical Imaging Computing, University College London, London, United Kingdom, ⁵Centre for Medical Image Computing, University College London, London, United Kingdom, ⁶Clinical Science, Department of Radiology, Lund University, Lund, Sweden, ⁷Inria Sophia Antipolis – Méditerranée, Université Côte d'Azur, Sophia Antipolis, France, ⁸Istanbul Technical University, Instanbul, Turkey, ⁹Therapanacea, Paris, France, ¹⁰Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ¹¹AGH University of Science and Technology, Krakow, Poland, ¹²LPI, ETSI Telecomunicacion, Universidad de Valladolid, Valladolid, Spain, ¹³Department of Biomedical Engineering, Linköping University, Linköping, Sweden, ¹⁴Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden, ¹⁵School of Instruments and Electronics, North University of China, Taiyuan, China, ¹⁶Department of Physics, University of Texas at Arlington, Arlington, TX, United States, ¹⁷NVIDIA Corporation, Bethesda, MD, United States, ¹⁸National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, United States, ¹⁹Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium, ²⁰Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, ²¹Leibniz Institute for Materials Engineering - IWT, University of Bremen, Bremen, Germany, ²²Department of Psychology and the eScience Institute, University of Washington, Seattle, WA, United States, ²³QMENTA Inc, Barcelona, Spain, ²⁴Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States

The acquisition and modelling of diffusion MRI (dMRI) data offer many opportunities to explore the organization of the brain. A variety of methods has been proposed to this end, but their generalizability to different diffusion encodings and stronger gradients remains unknown. In the MEMENTO challenge, we asked participants to predict dMRI signals collected with single, double and double oscillating diffusion encodings in combination with a variety of weighting parameters. We received eighty-three submissions ranging from signal representations to multicompartment models and deep learning-based predictions, which offer a unique perspective to investigate the status quo of the field.

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