Dr Marco Palombo

Overview

Biography

I am currently UKRI Future Leaders Fellow and Senior Lecturer (Associate Professor) in microstructure imaging at Cardiff University, with a joint appointment between the Cardiff University Brain Research Imaging Centre (CUBRIC) in the School of Psychology and the School of Computer Science and Informatics, where I co-lead the MicroTeam group. 

I am also member of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) at Cardiff University and co-chair of the "Human-centric AI for Medical Imaging" Working Group.

I am a physicist with expertise in biophysical modelling, machine learning, computational modelling, medical imaging, and data analysis.

Research

My research interest is in combining Physics, Computer Science and Neuroscience to develop non-invasive imaging technologies for early diagnosis and prognosis of neurological and psychiatric conditions.

In particular, my research programme combines computational modelling, MRI and modern machine learning to pioneer next generation healthcare technology for in vivo imaging of brain tissue. Towards this goal, I have made a few key innovations in microstructure imaging and non-invasive histology of the brain (with particular focus on grey matter): the first demonstration of non-invasive quantification of complex brain cell morphology, using diffusion-weighted MR spectroscopy and computational modelling (Palombo et al., PNAS 2016); mapping of cell body size and density, using SANDI (Palombo et al. Neuroimage 2020); mapping of water exchange, using NEXI (Jelescu et al. Neuroimage 2022); imaging restriction and exchange effects using diffusion temporal ratio, TDR (Warner et al. Neuroimage 2023), as well as first-of-its-kind generative models of complex brain cell morphologies (Palombo et al., Neuroimage 2019) and axonal bundles (Callaghan et al. Neuroimage 2020) for the controlled and flexible generation of ultra-realistic computational models of the brain microstructure, essential for more realistic numerical simulations (e.g. Monte Carlo).

Recent work focuses on combining such computational models, Monte Carlo simulations and machine learning for next generation microstructure imaging, e.g. maps of axonal permeability as new imaging marker of demyelination (Hill et al. Neuroimage 2021); in vivo quantification of glial reactivity (Ligneul et al., Neuroimage 2019, Genovese et al., NMR Biomed 2021); cancer-specific microstructure imaging to assess brain tumor’s response to radio/proton therapy (Buizza et al., Medical Physics 2020, Morelli et al. Medical Physics 2023); joint estimation of diffusion and relaxation properties of prostate cancer (Palombo et al. Nature Sci. Rep. 2023).