Proton path reconstruction for proton computed tomography using neural networks

Abstract

The most likely path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography. 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.

Enhanced Proton Tracking with ASTRA Using Calorimetry and Deep Learning

“…Various designs have been proposed over the years, see Refs. [5][6][7][8][9][10], to pave the way forward. Recently, A Super Thin RAnge (ASTRA) telescope has been proposed as a next-generation detector for pCT, its main advantages being its speed (aims at 100 MHz) and its fine segmentation (3 × 3 mm 2 bars) meant to accurately reconstruct the proton energies by range and to efficiently deal with pile-up.…”