A proton deposits energy as it traverses matter and this increases as it slows down, resulting in maximal energy deposition at the end of its path as illustrated in Figure. This energy, or dose, delivers the cancer-killing effect but can also be harmful to surrounding healthy tissue. The objective for PBT is to deliver the intended dose to the tumour, predetermined by previous clinical evidence, whilst minimising exposure to surrounding tissue. PBT has the potential to deliver a superior dose profile to more traditional photon treatments [9], however there are fundamental challenges in the treatment planning and verification that prevent its widespread use. Small day-to-day changes in patient anatomy, caused by water retention for example, can introduce uncertainty. Current practice is to account for uncertainties at the planning stage by either increasing the irradiated volume, which immediately reduces the tissue sparing potential of PBT, or compromising tumour coverage to ensure critical surrounding tissues are spared.
This project aims to make use of state-of-the-art numerical methods to design efficient proton transport models. Making use of optimal control theory we will compute the optimised dose in treatment planning to the tumour whilst sparing surrounding tissue. Uncertainty quantification techniques will then allow us to to account for treatment planning uncertainties during the optimisation process.
A proton deposits energy as it traverses matter and this increases as it slows down, resulting in maximal energy deposition at the end of its path as illustrated in Figure. This energy, or dose, delivers the cancer-killing effect but can also be harmful to surrounding healthy tissue. The objective for PBT is to deliver the intended dose to the tumour, predetermined by previous clinical evidence, whilst minimising exposure to surrounding tissue. PBT has the potential to deliver a superior dose profile to more traditional photon treatments [9], however there are fundamental challenges in the treatment planning and verification that prevent its widespread use. Small day-to-day changes in patient anatomy, caused by water retention for example, can introduce uncertainty. Current practice is to account for uncertainties at the planning stage by either increasing the irradiated volume, which immediately reduces the tissue sparing potential of PBT, or compromising tumour coverage to ensure critical surrounding tissues are spared.
This project aims to make use of state-of-the-art numerical methods to design efficient proton transport models. Making use of optimal control theory we will compute the optimised dose in treatment planning to the tumour whilst sparing surrounding tissue. Uncertainty quantification techniques will then allow us to to account for treatment planning uncertainties during the optimisation process.
