Mário João Fartaria1,2, Guillaume Bonnier1,2, Tobias Kober1,2,3, Alexis Roche1,2,3, Bénédicte Maréchal1,2,3, David Rotzinger2, Myriam Schluep4, Renaud Du Pasquier4, Jean-Philippe Thiran2,3, Gunnar Krueger2,3,5, Reto Meuli2, Meritxell Bach Cuadra2,3,6, and Cristina Granziera1,4,7
1Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Neuroimmunology Unit, Neurology, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 5Siemens Medical Solutions USA, Inc., Boston, MA, United States, 6Signal Processing Core, Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland, 7Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
Magnetic
Resonance Imaging(MRI) plays an important role for lesion assessment in early
stages of Multiple Sclerosis(MS). This work aims at evaluating the performance
of an automated tool for MS lesion detection, segmentation and tracking in
longitudinal data, only for use in this research study. The method was tested
with images acquired using both a "clinical" and an
"advanced" imaging protocol for comparison. The validation was
conducted in a cohort of thirty-two early MS patients through a ground truth
obtained from manual segmentations by a neurologist and a radiologist. The use of the "advanced
protocol" significantly improves lesion detection and classification in
longitudinal analyses.
