Biophysical Signal Prediction for Translation of Population Neuroimaging

A hospital MRI scanner and a UK Biobank research scanner produce images that cannot be directly compared, even of the same person. This fellowship aims to fix that mismatch. The problem is one of translation. Population-level brain imaging resources, such as UK Biobank, now contain detailed scans from hundreds of thousands of people, linked to their health records. These scans can reveal how an individual’s brain deviates from normal, potentially flagging early signs of disease. But the cutting-edge technology and protocols used in these research studies do not match the compromised, real-world equipment and faster protocols used in hospital scanners. Clinicians cannot currently apply population-level insights to individual patient scans. This project will build a biophysical framework that links the underlying biology of brain tissue to the physics of the imaging measurement. The researcher will identify which biological changes drive the variations seen in UK Biobank scans, then design new hospital-grade imaging sequences that are sensitive to the same biological signals. Finally, the team will create a harmonisation method that allows direct comparison between new clinical data and existing population norms. If successful, a routine hospital scan could be compared against a vast reference database, giving radiologists a quantitative, biologically grounded readout of whether a patient’s brain tissue is abnormal—without needing to upgrade every hospital scanner.