Abstract #1169

Attention please: deep learning can reproduce fractional anisotropy microstructure maps with reduced input data but loses clinical sensitivity

Marta Gaviraghi¹, Antonio Ricciardi², Fulvia Palesi¹, Wallace Brownlee², Paolo Vitali^3,4, Ferran Prados^2,5,6, Baris Kanber^2,5, and Claudia A. M. Gandini Wheeler-Kingshott^1,2,7

¹Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom, ³Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy, ⁴Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy, ⁵Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom, ⁶Universitat Oberta de Catalunya, E-Health Center, Barcelona, Spain, ⁷Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, fractional anisotropyQuantitative maps obtained with diffusion weighted (DW) imaging such as fractional anisotropy (FA) are useful in pathologies. Often, to speed up acquisition time, the number of DW volumes acquired is reduced. We investigated the performance and clinical sensitivity of deep learning (DL) networks to calculate FA starting from different numbers of DW volumes. Using 4 or 7 volumes, clinical sensitivity was affected because no consistent differences between groups were found, contrary to our “one-minute FA” that uses 10 DW volumes. When developing DL for reduced acquisition data, the ability to generalize and biomarker sensitivity must be assessed.

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