Compact deep learning pipeline for particle track reconstruction in the pCT detector system
Proton computed tomography (pCT) requires both fast and accurate reconstruction of particle trajectories and kinetic energies to achieve clinically viable image formation. Traditional distance-based matching algorithms often fail under the combined effects of multiple Coulomb scattering and track crossings and most importantly many of them take too much computation time, motivating the use of lightweight deep learning models that can be evaluated rapidly. In this work, we develop a two-stage reconstruction pipeline consisting of (i) a neural-network-assisted tracking module and (ii) a kinetic-energy estimation model. For the tracking task, compact multilayer perceptrons are trained to predict the expected hit position in the subsequent detector layer, providing a physically informed prior that substantially reduces ambiguities in bipartite matching. Furthermore, ambiguous tracks are flagged and excluded from the final analysis. Our training data is provided by OpenGATE simulation toolkit, both for tracking and energy estimation, where we designed a fully connected network that processes detector hit information. This model predicts the incoming proton kinetic energy with sufficient accuracy for current pCT image reconstruction methods. The entire pipeline benefits from deep-learning parallelism and evaluates particle tracks fast enough for clinical time constraints. Together, these results demonstrate that compact deep learning models can reliably reconstruct particle trajectories and energies in a realistic pCT detector system, offering a computationally efficient and highly accurate alternative to traditional matching and tracking methods.