Multiple Sclerosis (MS), the most frequent disabling neurological disease of young adults in Europe and North America, is an automimmune disease of the central nervous system (CNS). In the brain and in the spine, MS leads to typical demyelinating lesions with sizes of few mm to several cm. Advances in medical imaging with Magnetic Resonance Tomography (MRT) at the fore-front, have provided a steadily improving spatiotemporal data base of MS. Yet, despite of intensive international efforts there is almost complete lack of suitable and objective parameters to determine a distinct MS subtype or to forecast individual disease courses. Following an innovative, interdisciplinary approach that combines neuroimaging and geoinformatics, this RISE project aims at establishing a methodology for quantitatively characterizing MS lesion patterns in space and time: from the neuroimaging pool of methods, we will use recent MS lesion extraction algorithms and brain geometry normalization software to generate normalized binary MS lesion models. From the geoinformatics pool of methods, we will focus on Object Based Image Analysis (OBIA) for spatiotemporal MS lesion pattern handling and on geostatistics for multidimensional MS lesion pattern analysis and –reproduction.
Besides Jörg Kraus (MS Neuroimmunology), Stefan Golaszewski (Neuroimaging), Mark McCoy (Neuroradiology) the project involves Z_GIS researchers Peter Hofmann (OBIA), who will be employed by the CDK RISE funds and Robert Marschallinger (Geostatistics). Project Kick-off was on November 4, 2013.