Abstract #3507

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

Francesco La Rosa^1,2,3, Germán Barquero^1,2,3, Omar Al-Louzi⁴, Bénédicte Maréchal^1,3,5, Tobias Kober^1,3,5, Jean-Philippe Thiran^1,3, Pascal Sati^4,6, Daniel S. Reich⁴, Pietro Maggi^7,8, Martina Absinta^4,9, Meritxell Bach Cuadra^1,2,3, and Cristina Granziera^10,11,12

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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