  The Most Likely Path formalism (MLP) is widely established as the most
statistically precise method for proton path reconstruction in proton computed
tomography (pCT). However, while this method accounts for small-angle Multiple
Coulomb Scattering (MCS) and energy loss, inelastic nuclear interactions play
an influential role in a significant number of proton paths. By applying cuts
based on energy and direction, tracks influenced by nuclear interactions are
largely discarded from the MLP analysis. In this work we propose a new method
to estimate the proton paths based on a Deep Neural Network (DNN). Through this
approach, estimates of proton paths equivalent to MLP predictions have been
achieved in the case where only MCS occurs, together with an increased accuracy
when nuclear interactions are present. Moreover, our tests indicate that the
DNN algorithm can be considerably faster than the MLP algorithm.
