The NIHR Cambridge BRC Imaging theme uses key medical imaging tools to improve:
- clinical diagnosis and earlier detection
- patient stratification (categorising patients who share similar characteristics)
- predicting and detecting response to therapy, and
- personalising health care (what to expect for an individual’s disease course).
Improving current medical imaging techniques will have substantial benefits to patients, including those with coronary heart disease, stroke or cancer, improving their outcomes, saving lives and reducing NHS costs. Combining new imaging methods with clinical data, AI techniques and data science will help deliver the NHS Long-Term Plan to diagnose disease earlier and treat it more precisely.
Imaging is central to the diagnosis of many major health conditions, and we are working with themes across the NIHR Cambridge BRC and specialists in academia and industry, from MRI and PET physicists to machine-learning experts in the Cambridge Mathematics of Information in Healthcare Hub (CMIH) and GE Healthcare, to improve diagnosis and prognosis through the integration of information from the patients’ electronic health record (EHR) with advanced image analysis and understanding methods.
We are also involved in several local and national networks, including the National Cancer Imaging Translational Accelerator and the UK7T national network, working together to bring these new imaging techniques to patients in the clinic.
The University of Cambridge Department of Radiology and Wolfson Brain Imaging Centre (WBIC) have a strong track record of clinical research, high-impact publications and academic and industrial collaborations, with over £20 million invested in state-of-the-art equipment since 2017.
Misexpression of inactive genes in whole blood is associated with nearby rare structural variants
Quantitative 23Na magnetic resonance imaging in the abdomen at 3 T
Using machine learning to model older adult inpatient trajectories from electronic health records data
Evaluation of Dynamic Contrast-Enhanced MRI Measures of Lung Congestion and Endothelial Permeability in Heart Failure: A Prospective Method Validation Study
Association of Collagen, Elastin, Glycosaminoglycans, and Macrophages With Tissue Ultimate Material Strength and Stretch in Human Thoracic Aortic Aneurysms: A Uniaxial Tension Study