Post-Doctoral Research Visit F/M Detection of spinal cord lesions from combinations of multiple MRI sequences in living patients with MS

Descrizione

Post-Doctoral Research Visit F/M Detection of spinal cord lesions from combinations of multiple MRI sequences in living patients with MS Sintesi in italiano (traduzione automatica): Il candidato selezionato si unirà al laboratorio di ricerca Empenn presso Inria-Irisa, situato a Rennes, Francia. Il laboratorio è affiliato a Inria, Inserm, CNRS e Università di Rennes I, e si occupa di imaging neurale e biomarcatori per malattie cerebrali. Le principali mansioni includono la collaborazione con ingegneri, ricercatori e clinici per la ricerca sulla sclerosi multipla (SM) e l'elaborazione delle immagini. È richiesta una laurea di dottorato in un campo pertinente, come ingegneria biomedica o neuroscienze. Il progetto Primus, finanziato dal Ministero della Salute francese, mira a sviluppare un sistema di supporto decisionale clinico per la diagnosi e il monitoraggio della SM, con un focus sulla segmentazione delle lesioni del midollo spinale da immagini MRI. Il candidato dovrà affrontare sfide significative legate alla complessità delle immagini e all'accuratezza dei modelli di deep learning sviluppati. The selected candidate will join the research lab Empenn in Inria-Irisa, located in Rennes, France. Empenn ( https://team.inria.fr/empenn ) is jointly affiliated with Inria, Inserm (National Institute of Health and Scientific Research), CNRS (INS2I institute), and the University of Rennes I. The Empenn group operates the Neurinfo imaging facility in the context of a partnership with the University Hospital of Rennes, Inria, the CNRS, and the Cancer Research Center. The team has access to several computing facilities (e.g. IGRIDA cluster) and established collaborations with other Inria/Irisa research teams in the field of machine learning. Our research lab consists of more than 20 researchers, faculty members, PhD students, engineers and interns, working in the field of image processing and neuroimaging. The team targets the detection and development of imaging biomarkers for brain diseases and focuses its efforts on translating this research to clinics and clinical neurosciences at large. The selected candidate will collaborate with the engineers, researchers and clinicians of the team involved in Multiple Sclerosis (MS) research and image processing. In recent years, the number of disease-modifying treatments for Multiple Sclerosis (MS) has augmented significantly (McGinley, Goldschmidt, and Rae-Grant 2021). In particular, highly effective second-line immunosuppressive treatments have become available and the number of first-line treatments has increased. However, these treatments are not without potential adverse effects. It is therefore crucial to prescribe the right treatment to the right patient, and to monitor its effectiveness and safety closely. Currently, Magnetic Resonance Imaging (MRI) plays a central role in this context. In particular, MRI allows: - the identification of MS lesions in particular regions of the central nervous system during the first years of the disease; - the identification of new hyperintense MS lesions between two longitudinal MRI scans i.e. at two different time points. The two above elements are central, each with their own contribution, to select a patient's initial treatment as well as to modify the treatment over time. The Empenn team is one of the leaders of the Primus project. Primus (standing for “Projection in Multiple Sclerosis” (PI: Prof Gilles Edan, Rennes University Hospital)) was granted by the French Ministry of Health in 2022. This project gathers together researchers, faculty members, clinicians and private companies, with the goal of developing a clinical decision support system for Multiple Sclerosis diagnosis and follow-up. One of our contributions is dedicated to the development of methods that allow for detection and segmentation of Multiple Sclerosis lesions from spinal cord MRI images acquired with current clinical protocols. It must be emphasized that MS lesion segmentation in spinal cord is a complex task due to some major challenges such as the size of the anatomical structures of interest (the spinal cord ~ 1cm diameter) and the occurrence of significant artifacts due to motion and respiration. Over the past years, we led several works in this area. Particularly, we developed several deep learning models for the segmentation of SC lesions either from T2 sagittal MRI acquisitions or from a pair of one T2 sagittal acquisition and one STIR sagittal acquisition, which is one of the most commonly used combination of spinal MRI sequence used in the clinical setting. Then we assessed the added value of this last model to improve the performance of radiologists (Lodé et al. European Radiology 2025). In this study, we showed that the sensitivity of radiologists was higher with the help of the automatic tool than without, without any decrease in precision. However, to date, the combinations of sequences taken into account by these models are limited and do not refle