Methods for fast and accurate MR-guided radiotherapy in lung cancer

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) Develop a deep learning based method for retrospective fat suppression.
3) Develop an MR-based system for visually guided breath holds.
4) Assess the impact of visual guidance on intra-fraction repeatability of breath holds.