UCL Centre for Medical Image Computing


Marco Palombo, UKRI Future Leader Fellow - AI-powered brain microstructure imaging

27 April 2020

After a gruelling 3-round selection process, UKRI awarded Marco a Future Leader Fellowship - for his research proposal to explore "AI-powered brain microstructure imaging".

three-component shift of paradigm


In Marco Palombo's own words:

Nature has built one of the most extraordinary machines we could ever conceive: the brain, using just basic cellular components of neurons and glia. Understanding how these individual components are designed (cell morphology) and assembled together (tissue microstructure) is the key to understanding both brain's structure and function, and more importantly its degeneration/dysregulation in diseases. However, it is currently impossible to quantify tissue microstructure in a non-invasive way. In fact, standard methods like histology can reveal microscopic characteristics of tissue architecture but at the cost of invasive interventions like biopsies and limited coverage of the investigated tissue, undermining diagnostic power.

This fellowship develops innovative computational methods and magnetic resonance (MR) techniques to reveal new non-invasive markers of brain microstructure. The ultimate goal is to provide non-invasive tools for improved diagnostic information as powerful as invasive techniques.

Towards this goal, I propose a three-component shift of paradigm to address key limitations of current MR for microstructure imaging: (i) employing detailed simulation of the tissue architecture to encode the forward problem (from tissue microstructure to MR signal), (ii) modern AI to solve the inverse problem and (iii) estimate of uncertainty to quantify ambiguity and significance of the results.

Although the fellowship focuses on neurological diseases, it also aims to initiate follow-on projects to explore other applications, like body cancer. Alternative contrast methods will extend the methods to other MR modalities beyond diffusion for complementary and additional information on healthy and diseased tissues.