[1078] Quantitative and Computational Neuroimaging Laboratory

The Quantitative and Computational Neuroimaging Laboratory develops and validates new MRI and CT imaging techniques, combined with advanced quantitative analysis methods, to better characterise neuro-oncological, neurodegenerative and peripheral neurological disorders. The goal is to establish robust, reproducible biomarkers that refine diagnosis, tailor treatment, and predict progression or recurrence, within a precision-medicine framework.

Our research is organised along three axes. First, the development of new imaging techniques: microstructural MRI for small-scale structures such as capillaries and intracellular blood products, advanced diffusion imaging (DTI, multi-compartment models, non-invasive assessment of the glymphatic system), and photon-counting CT for improved spatial resolution and tissue-contrast detection in brain and peripheral neurological lesions. Second, quantitative and computational analysis of imaging data: deep-learning algorithms for automated extraction of anatomical and functional features, conventional computational methods for tissue-structure quantification (lesion load in peripheral neuropathy, microvascular and intracellular network properties), and mathematical modelling of MR signal-decay processes and multiphoton/light-sheet microscopy data to quantify tumour heterogeneity, lesion severity, treatment response and recurrence risk. Third, the integration of these advanced imaging technologies into clinical practice: evaluation of feasibility and clinical impact in large patient cohorts, optimisation of ultra-high-field MRI protocols (7 Tesla), and standardisation of neuroimaging analysis to ensure reproducibility and transferability of quantitative biomarkers across institutions.

This interdisciplinary approach, combining biomedical modelling, tissue-structure quantification, artificial intelligence and clinical neuroradiology, aims to detect neurological disease before clinical symptoms emerge, tailor treatments to individual patient profiles, and accelerate the translation of imaging innovations into clinical practice.