The imaging theme is broadly defined – encompassing conventional medical imaging modalities including CT, MRI, Ultrasound and PET – as well as microscopy, magnetic resonance, Raman and optical spectroscopy, and novel imaging techniques using quantum dots.
The focus is on the development of new diagnostic and analytical techniques for a wide range of applications from cancer screening to neurological disorders, as well as new imaging modalities such as quantum-enhanced imaging.
Multi-parametic MRI for early-stage cancer detection
Treatment and patient outcomes for many common cancers are significantly improved by early detection. This meta-project aims to identify biomarkers for early-stage cancers and predict progression of small lesions to aggressive and invasive cancers based on multi-parametric imaging data combined with clinical data such as blood tests, biopsy results and patient history using artificial intelligence, image processing and analytical techniques.
Quantification of metabolites by magnetic resonance spectroscopy
Chemical imbalances play a crucial role in the development and progression of many medical conditions from neurological and psychological disorders ranging from depression to dementia, to metabolic disorders such as diabetes, to cancer. In vivo quantification of metabolites using magnetic resonance spectroscopy has significant potential to improve diagnosis and treatment by detecting such chemical imbalances. However, while many techniques have been developed, there are signficiant problems relating to accuracy, reliability and reproducibility of the results that limit applications in practice.
This project aims to address these issues, to assess and improve the reliability of existing techniques and develop novel techniques using calibrated test objects, simulations, analytical techniques and AI.
Raman spectroscopy for cancer screening
Effective screening techniques play a key role in the early detection of cancers. This projects aims to develop blood tests for common cancers such as colorectal and breast cancer using Raman spectroscopy and AI to enable cost-effective population-level screening, earlier detection of cancers and improved treatment.