Abstract #1035

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

Francesco Grussu^1,2, Stefano B. Blumberg², Marco Battiston¹, Andrada Ianuș³, Saurabh Singh⁴, Fiona Gong⁴, Hayley Whitaker⁴, David Atkinson⁴, Claudia A. M. Gandini Wheeler-Kingshott^1,5,6, 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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