Abstract #1825

QuaSI-MTR (qualitative scans for imaging MTR): deep-learned MTR from routine scans using U-nets.

Antonio Ricciardi^1,2, Francesco Grussu^1,3, Ferran Prados^1,2,4, Baris Kanber², Rebecca S Samson¹, Daniel C Alexander³, Declan T Chard^1,5, and Claudia A M Gandini Wheeler-Kingshott^1,6,7

¹NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ²Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ³Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ⁴Universitat Oberta de Catalunya, Barcelona, Spain, ⁵National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, United Kingdom, ⁶Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ⁷Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy

Magnetisation transfer ratio (MTR) is a popular MR-modality for the identification of brain anomalies in multiple sclerosis due to its sensitivity to myelin changes. It however requires dedicated sequences with long acquisition times, which make its applicability in clinics less feasible. In this work, deep learning U-net architectures have been used to extract MTR information directly from routine qualitative images, bypassing the need for specialised acquisitions. Results show strong correlation with MTR and agreement between regional distributions in normal appearing tissues, both in healthy controls and multiple sclerosis patients.

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