Conditional generative model for dose calculation in radiotherapy

Job description: Particle propagation through matter by Monte Carlo method (MC) is known for its accuracy but is sometimes limited in its applications due to its cost in computing resources and time. This limitation is all the more important for dose calculation in radiotherapy since a specific configuration for each patient must be simulated, which hinders its use in clinical routine. The objective of this thesis is to allow an accelerated and thrifty dose calculation by training a conditional generative model to replace a set of phase space files (PSF), whose architecture will be determined according to the specificities of the problem (GAN, VAE, diffusion models, normalizing flows, etc.). In addition to the acceleration, the technique would produce an important gain in efficiency by reducing the number of particles to be simulated, both in the learning phase and in the generation of particles for the dose calculation (modelapos;s frugality). We propose the following method: - First, for the fixed parts of the linear accelerator, the use of a conditional generative model would replace the storage of the simulated particles in a PSF, whose data volume is particularly large. The compactness of the model would limit the exchanges between the computing units without the need for a specific storage infrastructure. - In a second step, this approach will be extended to the final collimation whose complexity, due to the multiplicity of possible geometrical configurations, can be overcome using the model of the first step. A second conditional generative model will be trained to estimate the particle distribution for any configuration from a reduced number of simulated particles. The last part of the thesis will consist in taking advantage of the gain in computational efficiency to tackle the inverse problem, i.e. optimising the treatment plan for a given patient from a contoured CT image of the patient and a dose prescription.

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