Low-Dose Photon-Counting CT for Lung Cancer Detection and Quantitative Characterisation

Lung cancer remains a leading cause of cancer-related mortality, and CT imaging plays a central role in its detection, staging and monitoring. As imaging demand increases, particularly through lung cancer screening programmes, there is a growing need to reduce radiation dose while maintaining diagnostic accuracy.

This project will investigate the use of next-generation photon-counting CT (PCCT) technology to enable low-dose lung cancer imaging without compromising image quality or quantitative performance. The work will combine advanced 3D printed lung phantoms with clinical imaging data to systematically evaluate how radiation dose reduction affects detection of lung nodules and the reliability of quantitative imaging biomarkers.

The student will work within the Sheffield Platform for Imaging Research in Oncology (SPIRO), collaborating with experts in radiology, medical physics, engineering and artificial intelligence. The project offers opportunities to gain experience in medical imaging, computational analysis, 3D printing technologies and translational clinical research.

This work will contribute to the development of safer imaging protocols and support future large-scale studies in lung cancer screening and surveillance.

Proposed start date: 1st October 2026

Entry Requirements: Candidates must have a first or upper second class honours degree or significant research experience. A background in a relevant discipline such as physics, engineering, computer science, mathematics, or a biomedical science with strong quantitative components is desirable. Experience with programming (e.g. Python, MATLAB or R) and/or image analysis would be advantageous. An interest in medical imaging, data analysis and translational healthcare research is essential.