Machine learning methods for 3T & 7T MRI analysis of white matter and cortical lesions in Multiple Sclerosis

Description: We have developed jointly with our collaborators many machine learning methods for the detection and segmentation of white matter and cortical lesions based on MRI of Multiple Sclerosis. Our major contribution has been the development of a partial-volume method to detect small lesions, even in the earliest stages of the disease. Our methods have been developed for clinical MRI setting and also for more advanced MRI sequences at 3T and 7T. Furthermore, we also investigate support diagnosis and prognostic tools based on advanced MRI-based biomarkers (central vein sign and rim sign). This project is supported by European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie project TRABIT (agreement No 765148).

Investigators:  Nataliia Molchanova (CHUV/UNIL), Maxence Wynen (UNIL/UCL Louvain), Meritxell Bach Cuadra (UNIL)

Collaborators: Prof. C. Granziera (University Hospital Basel and University of Basel), Prof. P. Maggi (Hopitaux Universitaires Saint-Luc), Prof B. Macq (UCLouvain), Prof. R. Du Pasquier (CHUV), Prof. M. Absinta (Johns Hopkins University & Vita-Salute San Raffaele University), Dr F. La Rosa & Prof E. Beck (Mount Sinai), Dr O Al-Louzi & Prof. P. Sati (Cedars-Sinai), Prof. D. Reich (Translational Neuroradiology Section – NIH), Dr. T. Kober (Siemens Healthcare).