Dr Ludovica Brusaferri

I am a Senior Lecturer in Medical Imaging at the School of Engineering at London South Bank University (LSBU), within the Department of Computer Science and Informatics (CSI), a role I have held since November 2023.

My academic background is in Biomedical Engineering - I graduated from Sapienza University of Rome in 2016. I then earned my PhD in Medical Imaging Physics from University College London in 2020, with a research focused on developing mathematical algorithms for PET/MR quantification.

Following my PhD, I joined the A.A. Martinos Center for Biomedical Imaging at Harvard Medical School, a global leader in clinical neuroimaging, as a Postdoctoral Research Fellow, where I specialised in PET/MR imaging for the study of neuroinflammation and chronic pain.

My research sits at the intersection of technology and clinical care, focusing on advancing medical imaging through the integration of Artificial Intelligence (AI). Specifically, I work on Positron Emission Tomography/Magnetic Resonance (PET/MR), a hybrid imaging modality combining molecular and structural imaging, available for research since around 2010. This technology is particularly valuable for studying neurodegenerative conditions (e.g., Alzheimer’s disease) and neuroinflammatory processes (e.g., chronic pain), with the goal of developing transparent, reproducible methodologies for seamless clinical adoption.

My early work centred on developing and optimising mathematical algorithms for PET/MR quantification to improve image reconstruction and estimate physiological parameters accurately. At Harvard Medical School, I expanded this focus, applying technical advancements to clinical neuroimaging, particularly for identifying imaging biomarkers of chronic pain (e.g., migraine) and the mental health effects of pandemic-related stressors.

Since joining LSBU, I have broadened my research by integrating AI-driven solutions in collaboration with engineers and computer scientists to address clinical challenges. My goal is to bridge the gap between technological innovation and healthcare applications, such as non-invasive PET quantification, enhancing disease staging, diagnostic accuracy, and clinical decision-making to improve patient outcomes.