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Abstract #3701

Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols

Álvaro Planchuelo-Gómez1,2, Maxime Descoteaux3, Santiago Aja-Fernández2, Jana Hutter4, Derek K. Jones1, and Chantal M.W. Tax1,5
1CUBRIC, Cardiff University, Cardiff, United Kingdom, 2Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain, 3SCIL, Université de Sherbrooke, Sherbrooke, QC, Canada, 4Centre for Medical Engineering, Centre for the Developing Brain, King's College London, London, United Kingdom, 5Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Acquisition, Quantitative ImagingMulti-contrast MRI is used to assess the biological properties of tissues, but excessively long times are required to acquire high-quality datasets. To reduce acquisition time, physics-informed Machine Learning approaches were developed to select the optimal subset of measurements, decreasing the number of volumes by approximately 63%, and predict the MRI signal and quantitative maps. These selection methods were compared to a full data-driven and two manual strategies. Synthetic and real 5D-Diffusion-T1-T2* data from five healthy participants were used. Feature selection via a combination of Machine Learning and physics modelling provides accurate estimation of quantitative parameters and prediction of MRI signal.

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Keywords