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Abstract #0960

Acquiring and predicting MUlti-dimensional DIffusion (MUDI) data: an open challenge

Marco Pizzolato1, Marco Palombo2, Elisenda Bonet-Carne2,3, Francesco Grussu2,4, Andrada Ianus5, Fabian Bogusz6, Tomasz Pieciak6,7, Lipeng Ning8, Stefano B. Blumberg2, Thomy Mertzanidou2, Daniel C. Alexander2, Maryam Afzali9, Santiago Aja-Fernández7,9, Derek K. Jones9,10, Carl-Fredrik Westin8, Yogesh Rathi8, Steven H. Baete11,12, Lucilio Cordero-Grande13, Thilo Ladner14, Paddy J. Slator2, Daan Christiaens13,15, Jean-Philippe Thiran1,16, Anthony N. Price13, Farshid Sepehrband17, Fan Zhang8, and Jana Hutter13
1Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom, 3BCNatal Fetal Medicine Research Center, Barcelona, Spain, 4Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 5Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, 6AGH University of Science and Technology, Kraków, Poland, 7Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain, 8Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 9Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, University of Cardiff, Cardiff, United Kingdom, 10Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia, 11Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, New York, NY, United States, 12Center for Advanced Imaging Imaging, Innovation and Research, New York University School of Medicine, New York, NY, United States, 13Centre for Medical Engineering, Centre for the Developing Brain, King's College London, London, United Kingdom, 14Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland, 15Department of Electrical Engineering (ESAT-PSI), KU Leuven, Leuven, Belgium, 16Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, 17Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

The variety of possible combinations of acquisition parameters is key to the versatility of MRI as a diagnostic modality. However, the full exploration of the parameter space defined by b-values, gradient directions, inversion and echo times comes at the expense of the acquisition time. We present the results of an open challenge where different methods were proposed to predict the content of a densely sampled acquisition, which explores such parameter space, from only a subset of parameter combinations. These indicate the possibility of leveraging the redundancy in the data to shorten the acquisition time while minimizing information loss.

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