Medical Physics and Biomedical Engineering


Paediatric Radiotherapy and Imaging Group (PRIma)

A picture of six women standing together smiling for the camera, the image contains Dr Catarina Veiga and her research group

About us

Radiotherapy is a very effective and a very commonly used type of cancer treatment where high doses of radiation are targeted to kill tumour cells. However, radiation does not only damage cancer cells; it can also damage nearby healthy cells, leading to side effects of treatment. Children are a patient group particularly vulnerable to side-effects of radiation-, which may appear years to decades later.

At PRIma, our research ultimately aims to reduce the incidence of side effects later in life from having received radiotherapy during childhood for cancer treatment.

Our group has multidisciplinary expertise in physics, engineering, and oncology, and specialises in using medical imaging analysis together with disruptive technologies like 3D-printing and artificial intelligence (AI) to improve the precision of radiotherapy delivery and to understand the development of radiation induced side-effects.

Research topics

Risk modelling of radiation-induced late effects

Some organs are more sensitive to the harmful effects of radiation than others; likewise, some patients are more sensitive than others. We develop novel methodologies for advanced 3D risk modelling to radiation-induced late effects in children and perform risk assessment studies to support the use of novel radiotherapy techniques in the treatment of some paediatric cancers.

Key publications: Veiga et al (2021) (https://iopscience.iop.org/article/10.1088/1361-6560/abf010), Taylor et al (2021) (https://doi.org/10.1016/j.phro.2021.06.003)

Computational and 3D-printed phantom development 

Dose measurement in real patients is challenging, so computational and physical phantoms are commonly used in patient and organ motion simulation, dosimetry evaluations, quality assurance of therapeutic and diagnostic irradiation, epidemiological studies, and analysis of dose-response relationships. We develop phantoms of average childhood cancer patients and use them to design novel 3D-printable phantoms that closely mimic how paediatric tissues interact with radiation.

Monte Carlo dosimetry for proton therapy

Accurate dose calculation is essential for ensuring high-quality treatments in clinical settings. It is also fundamental to develop novel predictive models of radiation-induced toxicity to make future radiotherapy treatments increasingly personalised. We develop accurate Monte Carlo dose model of clinical proton plans for clinical use and for epidemiological data collection.

Key publications: Botnariuc et al 2024 (https://iopscience.iop.org/article/10.1088/1361-6560/ad1272)

Image-guided and AI-powered radiotherapy

The precision of radiotherapy delivery is affected by day-to-day changes in patient positioning and internal anatomy. Children are particularly susceptible to daily anatomical variations. We develop AI-powered solutions to image-guidance and treatment adaptation tailored to young patients, ensuring accurate dose delivery verification while minimising long-term risks associated with repeat ionising imaging.

Key publications: Szmul et al (2023) (https://iopscience.iop.org/article/10.1088/1361-6560/acc921), Taylor et al (2023) (https://doi.org/10.1259/bjr.20230058)


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Royal Academy of Engineering


CRUK RadNet City of London

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Wellcome Trust


National Institute for Health and Care Research (NIHR )


UCL i4Health

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Engineering and Physical Sciences Research Council (EPSRC)


FCT: Foundation for Science and Technology (Portugal)