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Abstract #3507

Automated assessment of paramagnetic rim lesions in multiple sclerosis patients with 3T and 7T MP2RAGE

Francesco La Rosa1,2,3, Germán Barquero1,2,3, Omar Al-Louzi4, Bénédicte Maréchal1,3,5, Tobias Kober1,3,5, Jean-Philippe Thiran1,3, Pascal Sati4,6, Daniel S. Reich4, Pietro Maggi7,8, Martina Absinta4,9, Meritxell Bach Cuadra1,2,3, and Cristina Granziera10,11,12
1Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland, 3Radiology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 4Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, 5Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 6Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 7Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 8Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium, 9Department of Neurology, Johns Hopkins University, Baltimore, MD, United States, 10Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 11Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 12Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland

Paramagnetic rim lesions (PRL) in multiple sclerosis are chronic inflammatory lesions depicted in susceptibility-based MRI where high PRL lesion burden has been associated with a more aggressive disease course. As their visual detection is subjective and time-consuming, a convolutional neural network (RimNet) has recently been developed and applied to 3T susceptibility contrast MR images. In this work, we evaluate a unimodal RimNet architecture based on either the MP2RAGE uniform contrast or the concurrently obtained T1 map at both 3T and 7T. Results show that prediction improves considerably at 7T, suggesting that 7T MP2RAGE might be helpful for automatically identifying PRL.

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