Seminar Details
2020-09-22 (14h) : Mathematics in medical physics for radiation oncology
At Euler building (room A.002) + MS Teams
Organized by Mathematical Engineering
Speaker :
Lee, John Aldo
Abstract :
Radiation oncology treats cancer by irradiating patients locally with ionizing beams, like X-rays, protons or even heavier particles.
Treatment planning can be seen as a ballistic problem whose solution is such that beams reach a certain dose on a target volume to control the tumor, while they do not exceed some threshold dose in organs at risk to limit adverse effects.
In other words, treatment planning can be translated into a multi-objective optimization problem or a constrained optimization problem, the former allowing better "tweakability" to satisfy complex clinical objectives.
Current research in radiation oncology, including in the UCLouvain/MIRO group, aims at modeling treatment uncertainties with multiple error scenarios in minimax problems, to make treatment more robust against those.
Other lines of investigation go towards faster, more user-friendly multi-objective interaction with Pareto frontier pre-calculation.
Yet another direction aims at improving accuracy in objective function evaluation by replacing fast "analytical" dose computation with Monte Carlo simulations.
Depending on simulation speed, hybrid optimization schemes can be devised, interleaving slow, accurate checkpoints of the objective function with faster, cheaper surrogates.
Further perspectives include clinical decision support system involving artificial intelligence.
This entails automating the few last manual steps in treatment planning, simulation, and evaluation with respect to clinical endpoints.
Such automation consists for instance in predicting the optimal planned dose with image-to-image deep convolutional neural networks like U-net.
In these long workflows, though, the challenge is to evaluate how uncertain the prediction is and how it propagates till the endpoints the physicians consider to make their treatment decision.
Most treatment workflows feed on medical images, whose quality is key, and lately reconstruction methods rely more and more on inverse problem solving to overcome the pitfalls of classical algebraic reconstruction.