Therapeutic Modelling in Multiple Sclerosis: Atrophy and White Matter Hyperintensities
Recent advances in the field of disease progression modelling have enabled the development of fully data driven models of disease through the use of surrogate imaging biomarkers of the underlying pathophysiological processes. These models commonly assume linearly progressive forms of pathology, ignoring therapeutic effects and disease variability over time. Also, they are highly dependent on the ability to extract robust, reproducible and longitudinally stable biomarkers from images, a complex task in Multiple Sclerosis (MS).
This project will tackle the exciting challenge of extracting relevant, informative and longitudinally consistent biomarkers from longitudinal MRI scans of patients with MS. These biomarkers, characterising aspects such as atrophy (shrinkage) of regions of the brain and the volume/location of MS white mater lesions, will then be used to model the different patterns of MS pathology and the effect of therapy in modifying the progression of disease. The tools and models developed as part of this project are to be integrated within the clinical workflow, informing clinical decisions and optimising the process of therapeutic escalation.
This project involves a balanced mixture of classical “image analysis” research (e.g. image segmentation, registration and morphometric characterisation) and advanced “computational modelling” tools (E.g. Bayesian models) to characterise disease progression and therapeutic effects.
It is highly aligned with the UCLH BRC Imaging Initiative as a platform which aims to explore very large neurological and neuroradiological datasets currently available on the PACS and RIS, and the NHNN Quantitative Neuroradiology Initiative which aims to deploy novel subject-specific computational methods and biomarker reports within a clinical setting.