SUPAF03
CERN, Meyrin, Switzerland
IBA, Louvain-la-Neuve, Belgium
UCL, Louvain-la-Neuve, Belgium
ULB - FSA - SMN, Bruxelles, Belgium
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SUPAF03 |
Optimization of Hadron Therapy Beamlines Using a Novel Fast Tracking Code for Beam Transport and Beam-Matter Interactions | |
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| The optimization of proton therapy beamlines challenges the traditional approach used in beam optics due to the very strict constraints on beam quality, especially for Pencil Beam Scanning, despite the large losses induced by the emittance increase coming from the energy degrader. In order to explore the performances of proton therapy beamlines, we proceed using a new fast beam tracking Python library coupled with a genetic algorithm. Global optimization algorithms such as the genetic algorithm or basin hopping schemes require numerous evaluations of the model and their practical implementations are limited by the computation time at each iteration. To overcome this limitation, while at the same time allowing an open-box user experience, a Python library has been developed, including transport models for the typical hadron therapy beamlines elements, as well as models for the computation of multiple Coulomb scattering. The Multi-Objective Genetic Algorithm (MOGA) allows to explore the parameter space in a global sense. This multi-objective algorithm enables the simultaneous optimization of complex constraints specific to proton therapy beamlines. Results for the IBA Proteus One system are presented and discussed in detail. | ||
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Slides SUPAF03 [9.526 MB] | |
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