Overview
This interdisciplinary PhD project aims to develop the next generation of low-dose, mobile 3D X-ray systems using digital tomosynthesis and advanced simulation. Working with Adaptix Ltd and the QUASAR Group, you will advance innovative imaging geometries, AI-enhanced reconstruction, and experimental prototypes for clinical and industrial applications.
About this opportunity
Digital tomosynthesis (DT) offers rapid, low-dose 3D imaging using compact and mobile X-ray systems, filling the performance gap between planar radiography and full CT. The QUASAR Group has collaborated with Adaptix Ltd for more than a decade to develop novel 3D imaging technologies, including the recently FDA-cleared Adaptix Ortho350 extremity imaging system. Related systems have already been commercialised in veterinary and industrial imaging.
This PhD project will build on the new SCIMITAR framework (Hill et al., Biomed. Phys. Eng. Express, 2025), which integrates geometric simulation with genetic-algorithm optimisation to design and evaluate next-generation chest DT devices. You will work within a multidisciplinary team with expertise in simulation, medical physics, imaging hardware, and AI-based reconstruction. The precise research direction will be defined collaboratively, but potential areas include:
- Simulation and digital twinning: extending SCIMITAR for full 3D optimisation, dose estimation, and patient-specific adaptation.
- Radiation transport modelling: using Monte Carlo and physics-based digital twins to evaluate imaging geometries, collimation strategies, and safety trade-offs.
- Novel source and detector technologies: investigating dual-energy approaches, alternative detector architectures, and cold-cathode (CNT) X-ray emitters in partnership with Adaptix Labs.
- AI-driven analysis: developing machine-learning algorithms for image reconstruction, artefact reduction, and automated feature detection from DT datasets.
- Synthetic patient populations: simulating diverse anatomies and imaging workflows to assess diagnostic accuracy and robustness.
- Experimental validation: acquiring data using phantoms and prototype Adaptix chest imaging systems, and exploring system miniaturisation, source motion strategies, and adaptive cone-angle designs.
Training & Structure:
Year 1 will focus on training in radiation transport, dosimetry, Monte Carlo simulation, CAD modelling, image reconstruction, and AI methods. Years 2–3 will involve independent research, optimisation studies, algorithm development, and experimental data collection. The final phase will centre on system integration, validation, and thesis preparation.
You will receive extensive training in medical and accelerator physics, radiation dosimetry, simulation and optimisation (Monte Carlo, digital twins, genetic algorithms), imaging hardware characterisation, and advanced data analysis. The project includes substantial collaboration with Adaptix Ltd, with time spent at both the University of Liverpool (Cockcroft Institute) and Adaptix’s Oxford laboratories.
Funding covers 42 months of support, including tuition fees, UKRI-aligned stipend, travel, conference participation, and experimental materials.
Further reading
Hill, J. et al. (2025). SCIMITAR: A framework for geometric simulation and genetic-algorithm optimisation of digital tomosynthesis systems. Biomed. Phys. Eng. Express.
https://iopscience.iop.org/article/10.1088/2057-1976/ae0fa0
Additional relevant topics: digital tomosynthesis, mobile 3D X-ray imaging, Monte Carlo modelling, genetic algorithms, detector design, cold-cathode X-ray emitters.