Publié le 5 mars 2018 | Mis à jour le 10 février 2020

Brent HUISMAN 2013-2016 - PhD director: David Sarrut, co-director: Etienne Testa


Title: Towards real-time treatment control in protontherapy using prompt-radiation imaging: simulation and system optimization

The first part of this PhD program is the development, analysis and release of a variance reduction method for the simulation of prompt gammas (PGs) in clinical proton therapy simulations. The variance reduction method (named vpgTLE) is a two-stage track length estimation method developed to estimate the PG yield in voxelized volumes. As primary particles are propagated throughout the patient CT, the PG yields are computed at each step, resulting in a voxelized image of PG production yield. The second stage uses this intermediate image as a source to generate and propagate the number of PGs throughout the rest of the scene geometry, e.g. into a detection device, corresponding to the number of primaries desired. For both a geometrical heterogeneous phantom and a complete patient CT treatment plan with respect to analog MC, at a convergence level of 2\% relative uncertainty in the 90\% yield region, a gain of around $10^3$ was achieved. The method agrees with reference analog MC simulations to  within $10^{-4}$, with negligible bias.
The second part of this PhD program is the study of PG fall-off position (FOP) estimation in clinical simulations. The number of protons (spot weight) required for a consistent FOP estimate was investigated for two PG cameras, a multi-parallel slit and a knife edge design, for a single spot of a fully clinical simulation of a patient treatment. By studying recent treatment plans from various proton clinics, we observe very few spots with weights over $10^8$. We did not manage to detect the morphological change present, an approximately 13mm shift, between the (RP)CT with either PG camera, with such statistics. Only for $10^9$ primaries, with one of the cameras, the change may be expected to be detected.A new spot-grouping method is proposed that combines better measurement statistics with fall-off preservation.
  • Auteur(s)
    Brent HUISMAN