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(EPSRC DTP) Deep learning methods for fast and accurate MR-guided radiotherapy in lung cancer

Project Description

Lung cancer is the third most common cancer in the UK with around 45,000 new patients per year. Survival remains poor, with a third of patients experiencing local failure and a 5 year survival of only 9.5%. Radiotherapy is an important component of treatment, given to >50% of patients. Studies have shown that elevating tumour dose can improve local control, however nearby normal tissues that are sensitive to radiation, such as the heart, limit the radiotherapy dose that can be delivered safely.

Most patients receive radiotherapy in 20-33 daily fractions. Before each fraction the patient must be imaged to ensure they are correctly positioned relative to the intended treatment beams. This also allows the treatment to be adjusted if necessary, for example if the patient has lost weight.

The recently introduced magnetic resonance linear accelerator (MR-Linac) provides simultaneous MR imaging and radiation. This allows patients to benefit from the superior image quality of MRI compared to CT, which improves the targeting of disease and sparing of healthy tissue. It also offers high quality, real-time imaging to track the tumour during irradiation.

The primary limitation of the MR-Linac is the long treatment times, which can exceed an hour per fraction, due to the complex workflow currently required. Although MRI setup images offer improved visibility for treating and sparing of essential organs, acquiring these images takes longer than CBCT on a standard linear accelerator. Spending a long time on the treatment couch is uncomfortable for the patient and increases the risk of movement of the patient which can make the position correction take even longer. There is therefore an urgent clinical need for the implementation of novel imaging techniques which can accelerate on board image acquisitions, but without the artefacts normally caused by shorter imaging times and motion. This project will apply the latest concepts in accelerated MR imaging to lung cancer and bring these techniques into clinical practice.

In this project, the student will:
1) Use the JEMRIS simulator to create a digital thorax model which can be used to test out different imaging techniques.
2) Design optimal imaging sequences for the key image contrasts required and implement these sequences on the MR-Linac.
3) Develop and train a Deep Learning neural network to allow fast, artefact-free reconstruction of the accelerated sequences.
4) Work closely with the clinical lung cancer team to ensure rapid translation of the developed techniques into the clinic.

Entry Requirements:
Candidates must hold, or be about to obtain, a minimum upper second class (or equivalent) undergraduate degree in relevant subject. A related master’s degree would be an advantage.

On the online application form select PhD Cancer Sciences. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (

Funding Notes

EPSRC DTP studentship with funding for a duration of 3.5 years to commence in September 2019. The studentship covers UK/EU tuition fees and an annual minimum stipend (£15,009 per annum 2019/20). Due to funding restrictions the studentship is open to UK and EU nationals with 3 years residency in the UK.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.


McWilliam, A., Rowland, B., & van Herk, M. (2018). The Challenges of Using MRI During Radiotherapy. Clinical Oncology, 30(11), 680–685.

van Herk, M., McWilliam, A., Dubec, M., Faivre-Finn, C., & Choudhury, A. (2018). Magnetic Resonance Imaging-Guided Radiation Therapy: A Short Strengths, Weaknesses, Opportunities, and Threats Analysis. Radiation Oncology Biology, 101(5), 1057–1060.

Johnson, C. N., Price, G. J., Faivre-Finn, C., Aznar, M. C., & van Herk, M. (2018). Residual setup errors towards the heart after image guidance linked with poorer survival in lung cancer patients: do we need stricter IGRT protocols? International Journal of Radiation Oncology, Biology, Physics, 102(2), 434–442.

McWilliam, A., Lee, L., Harris, M., Sheikh, H., Pemberton, L., Faivre-Finn, C., & van Herk, M. (2018). Benefit of using motion compensated reconstructions for reducing inter-observer and intra-observer contouring variation for organs at risk in lung cancer patients. Radiotherapy and Oncology, 126(2), 333–338.

Emerging Role of MRI for Radiation Treatment Planning in Lung Cancer. Cobben DC, de Boer HC, Tijssen RH, Rutten EG, van Vulpen M, Peerlings J, Troost EG, Hoffmann AL, van Lier AL. Technol Cancer Res Treat. 2016 Dec;15(6):NP47-NP60. Epub 2015 Nov 19. Review.

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