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Abstract #1035

SARDU-Net: a new method for model-free, data-driven experiment design in quantitative MRI

Francesco Grussu1,2, Stefano B. Blumberg2, Marco Battiston1, Andrada Ianuș3, Saurabh Singh4, Fiona Gong4, Hayley Whitaker4, David Atkinson4, Claudia A. M. Gandini Wheeler-Kingshott1,5,6, Shonit Punwani4, Eleftheria Panagiotaki2, Thomy Mertzanidou2, and Daniel C. Alexander2
1Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 2Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 3Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, 4Centre for Medical Imaging, University College London, London, United Kingdom, 5Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 6Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy

This work introduces the “Select and retrieve via direct up-sampling” network (SARDU-Net), a new method for model-free, data-driven quantitative MRI (qMRI) experiment design. SARDU-Net identifies informative measurements within lengthy acquisitions and reconstructs fully-sampled signals from a sub-protocol, without prior information on the MRI contrast. It combines two deep networks: a selector, which selects a signal sub-sample, and a predictor, which retrieves input signals. SARDU-Net can be run with standard computational resources and can increase the clinical appeal of qMRI. Here we demonstrate its potential on qMRI of prostate and spinal cord, two areas where fast acquisitions are key.

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