Towards robust disability prediction in multiple sclerosis from brain MRI.

Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system. There is no cure for MS, but many treatments have been developed to slow down its progression. Disease progression monitoring and clinical decision making often rely on the expanded disability status scale (EDSS). Unfortunately, EDSS suffers from poor reliability, repeatability, and high inter-rater variability. A first goal of this project is to reduce the inter-rater variability and increase repeatability in quantifying the risk of patient disability by developing a machine learning technique based on anatomical magnetic resonance images (MRI) and diffusion MRI (dMRI). As a first step, we will focus on the prediction of the EDSS scoring, but other clinical scores will be included as well. To develop an automated EDSS scoring method, a large database is required. Such databases are typically composed of images from multiple centers, and hence depend on scanner hardware, reconstruction algorithms and acquisition protocols. These factors lead to high intra- and intersite variability in structural MRI data, and even more in parameters derived from dMRI data. A second goal is to develop, implement and validate harmonization methods for structural and dMRI data, to reduce unwanted variability while preserving biological variability. Working towards this goal, I co-authored a review paper on dMRI harmonization methods [Pinto, et al. 2020]. A next step is to validate a recently proposed diffusion harmonisation method "Method of Moments" [Huynh, et al. 2019] on in vivo dMRI data. Finally, as part of the Horizon 2020 initial training network B-Q MINDED, the project's ultimate goal is the integration of the harmonisation and EDSS scoring algorithms in a product that can be used in clinical trials and, in a later stage, in the daily clinic. Roadmap September 2021-October 2021: Finalizing of the EDSS scoring application based on anatomical MRI. Submission of a journal manuscript "Prediction of EDSS scores in MS patients from MRI" by the end of October 2021. November 2021-December 2021: Finalizing implementation and validation of deep-learning approaches for the harmonisation of anatomical and diffusion MR images. January 2022 - February 2022: Automated EDSS scoring based on harmonized structural and dMRI data. March 2022-July 2022: preparation of the PhD thesis. References Pinto, M.S., Paolella, R.,…..et al. "Harmonization of brain diffusion MRI: Concepts and methods." Frontiers in Neuroscience 14 (2020). Huynh, Khoi Minh, et al. "Multi-site harmonization of diffusion MRI data via method of moments." IEEE transactions on medical imaging 38.7 (2019): 1599-1609.

Keywords:MAGNETIC RESONANCE IMAGING (MRI)

Disciplines:Medical informatics