MaThRad Workshop discussion: Monte Carlo

On the 9th and 10th of February, the first MaThRad Clinical Workshop was held in London, where dozens of academics, from numerous disciplines, gathered to exchange ideas, learn about each other’s fields, and develop collaborative projects which MaThRad could undertake.

The first area of interdisciplinary projects is focused on Monte Carlo methods in both a mathematical and applied context.

Propagation of uncertainties

We wish to understand how uncertainty in the input data propagates through a Monte Carlo simulation and estimate the effect on the solution. This could be applicable to radiation  therapy (proton and photon) therapy clinics to develop site specific uncertainty models beyond range uncertainties and systematic and random  setup errors by quantifying the personalised impact of physical and biological uncertainties in customising radiotherapy treatment planning – uncertainty-driven treatment personalisation.

Speed up Monte Carlo codes

The existing fast Monte Carlo codes are not fast enough for advanced optimisation in treatment planning, such as 4D robust optimisation and linear-energy-transfer (LET) guided robust optimisation (for on-the-day adaptation of treatment). A potential solution might be to consider AI at the level of particle transport. This can potentially improve prospective planning, as the faster simulation time would allow the clinician to account for expected anatomy changes, translations/rotations, and range uncertainty in their simulation. Additionally, this may enable a fast review and dosimetric assessment of online imaging in radiotherapy.

Many of the current simulation methods in commercial treatment planning systems only transport primary particles. We would aim to develop a complete Monte Carlo simulation that accounts for these secondary particles (such as gamma rays and neutrons generated in nonelastic collisions) in the dose calculation. The goal would be to compare this complete simulation to primary particle only model and determine if there is a significant difference in the calculations. This would potentially increase the accuracy of the treatment planning and reduce the risk of secondary cancers. Additionally, as these secondary particles are detectable as they exit the patient, it would allow us to verify and validate the Monte Carlo simulations.

Cross-reference Monte-Carlo simulations

As mentioned above, we wish to expand the scope of the current Monte Carlo simulators to account for secondary particles (such as gamma rays generated in nonelastic collisions) in the dose calculation. However, in the context of proton and heavy ions-based patient radiotherapy treatments, these secondary particles are potentially detectable as they exit the body. In principle, this would allow us to independently verify the position of energy deposition and total radiation dose.

However, the gamma rays generated by nonelastic collisions are rare events, and thus to maximise the efficiency of data collection, radiation detectors must be placed in optimal positions. This could be the focus of another project which would assimilate the beam parameters and detector data, with the Monte Carlo simulation to optimise the position.

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