Meeting Banner
Abstract #1240

Optimising multi-contrast MRI experiment design using concrete autoencoders

Chantal Tax1,2, Hugo Larochelle3, Joao P. De Almeida Martins4, Jana Hutter5, Derek K. Jones2, Maxime Chamberland2, and Maxime Descoteaux6
1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2CUBRIC, Cardiff University, Cardiff, United Kingdom, 3Google Brain, Montreal, QC, Canada, 4Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway, 5Centre for Medical Engineering, King's College London, London, United Kingdom, 6SCIL, University of Sherbrooke, Sherbrooke, QC, Canada

Multi-contrast MRI provides a comprehensive picture of tissue microstructure, but the high dimensionality of the parameter space increases scan time. In this work, we present a data-driven approach to multi-contrast MRI experiment design using concrete autoencoders. Concrete autoencoders simultaneously perform measurement subset-selection and learn a prediction of the full set of measurements. This approach was evaluated on two multi-contrast databases encoding diffusion, relaxation, and susceptibility. The results showed similar patterns of measurement-subset selection and mean-squared errors across different training sets. The increasing availability of public multi-contrast MRI databases can further push data-driven approaches in providing recommendations for experiment design.

This abstract and the presentation materials are available to members only; a login is required.

Join Here