QUANTIMA: Quantitative imaging platform for the diagnosis, subtyping, staging and outcome prognosis in dementia

Analysis of conditions altering microstructural tissue integrity & cellular arrangement, such as dementia, is vital to personalise patient treatment & improved outcomes. Presently, it is often the case that these subjects are only identified after the disease is at a grossly advanced stage, making prognosis poor.

In this fellowship I aim to develop an innovative AI-enabled magnetic resonance (MR) image analysis platform, known as QUANTIMA, capable of generating new non-invasive biomarkers of brain microstructure & providing non-invasive tools for improved diagnostic information as powerful as invasive and/or expensive techniques such as CSF lumbar puncture & PET. Understanding the design (morphology) & arrangement (tissue microstructure) of the brain's individual components is the key to deciphering both its structure & function, and more importantly its degeneration/dysregulation in diseases.

The platform will make use of advanced diffusion-weighted magnetic resonance imaging (DW-MRI) based microstructure modelling techniques, providing an indirect but non-invasive probe of the tissue microstructure at the micrometre scale. However, tissue microstructure is highly complex while the DW-MRI signal is quite simple, so the mapping from signal to microstructure is ill-posed.

Current computational modelling techniques aimed at overcoming this challenge use mathematical models, mapping the DW-MRI signal to underlying tissue properties, to estimate those properties by fitting the models per voxel of the DW-MRI data. Nevertheless, these methods suffer from a number of limitations that have restricted their diagnostic power & clinical adoption, such as poor sensitivity to features of complex cellular morphologies, requirements for long MRI scan times & state-of-the-art hardware not commonly available in clinical settings, reliance on predefined models of cellular arrangement & biology trying to mimic healthy tissue, therefore limiting the methods' applicability to disease processes (especially unobserved), & unquantified ambiguities.

To overcome these limitations, the proposed platform will use advanced AI-based optimisations & accelerations, capable of generating quantitative estimates of tissue microstructure, comparable with the state-of-the-art in microstructure computational modelling, whilst overcoming their limitations by: i) relying on clinically achievable MR acquisition protocols applicable on commonly available MR hardware (1.5T & 3T scanners), ii) providing a model free approach that solely relies on the observations made using the diffusion MR signal, iii) is capable of estimating uncertainty, quantifying ambiguity & the significance of the results.

When diagnosing these conditions, aside from imaging, doctors rely on information gathered from the patient's medical history, physical examination, laboratory tests, and the characteristic changes in thinking, day-to-day function, & behaviour. Offering a unified solution, the platform will also be able to perform multimodal & multi parametric data fusion, utilising information from such data sources (e.g.,cognitive tests), allowing for earlier & accurate dementia diagnosis, subtyping, staging, and disease-trajectory prediction; therefore, enabling personalised treatment selection & improved outcomes.

The project will also lead to the development of a multiparametric dementia-specific optimised (for clinical use) MRI scan protocol, maximising information obtained over short scans & satisfying the requirements of the novel AI-enabled microstructure modelling technique that I intend to develop.

In collaborations with the London AI Centre for Value Based Healthcare, University of Manchester, Greater Manchester Mental Health NHS Foundation Trust, GSK, and Salford Royal NHS Foundation Trust, the platform will then be validated using a multifaceted approach: using both retrospective & prospective patients data, and through a clinical pilot study.