Tobias Kortus

"Tracking of Proton Traces in a Digital Tracking Calorimeter using Reinforcement Learning"

Proton computed tomography (pCT) poses an alternative imaging technique to conventional computed tomography, providing multiple technical benefits in particle therapy. Especially the direct calculation of RSP, as opposed to the indirect RSP calculation based on converting Hounsfield units, allows for reduced uncertainties during treatment planning, providing improved spatial precision. Due to interactions of particles, mainly influenced by multiple Coulomb scattering, pCT requires sophisticated algorithms for reconstructing proton tracks captured in the pCT scanner. We propose a novel ground-truth free track reconstruction scheme based on deep reinforcement learning (RL) allowing for the training of deep neural network architectures in a partial information setting using only raw sensor data. We show with results on a simplified detector that modeling the effects of multiple Coulomb scattering is a sufficient reward function for an efficient optimization of software agents, yielding strong empirical results on simulated data. Further, we prove that with the chosen network architecture we are able to generalize to previously unseen particle densities, further simplifying training procedures.

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