Theoretical considerations on the spatial resolution limit of single-event particle radiography.

Particle radiography (pRad) has been proposed and investigated as a promising tool for particle therapy as it provides a water equivalent thickness (WET) image of the patient. In single-event particle imaging, for each measured particle, the related most likely path (MLP) through the object is estimated to account for multiple Coulomb scattering (MCS). In previous studies, the accuracy limit of the MLP has been used to determine the spatial resolution limit. In this work, we investigate the limit of the spatial resolution achievable with different pRad algorithms based on a theoretical model of the particle scattering for an ideal beam and detector. First, we investigate binning the particles in a plane seated at the depth of the object of interest (plane-of-interest binning; PIB) and extend existing theoretical considerations also to objects not located in the binning plane. We use this to model the spatial resolution in case of binning the particles directly at the front or rear tracker (FTB and RTB, respectively). Further, we investigate evenly distributing the particles' WET along their trajectory into pixel channels and creating the pRad image as channel mean (along-path-binning; APB). Monte Carlo simulations are used to qualitatively investigate the different algorithms and to validate the theoretical predictions. We show that projecting the scattered particle paths onto a single image will inevitably result in a limited spatial resolution lower than expected from only the MLP uncertainty. Only in the case where the depth of a feature is known and used as binning depth for PIB, the spatial resolution of that feature is equal to the path estimation accuracy. For the APB algorithm the spatial resolution decreases with increasing depth in the object, especially if the true particle path through the object would be known. The derived theoretical models will be useful for future development of improved pRad reconstruction algorithms.