Proton CT nowadays aims at improving hadron therapy treatment planning by mapping the stopping power of materials. In order to optimize a spatial resolution of the reconstructed images, the most likely path (MLP) of each proton can be computed. We investigated the errors in the computation of this path due to the configuration of the system, i.e. the spatial resolution of the tracking planes, their material budget, and their positioning. A method for computing the uncertainty in the estimated paths for a given system was derived. The uncertainties upon the entrance and exit of the object were propagated analytically in the path computation. This procedure was then used to evaluate the impact of each parameter, and to compare different systems. We show that the intrinsic characteristics of the system generate an uncertainty in the positions and directions of the particles propagated during the MLP computation. The spatial resolution and material budget of the trackers in particular may affect the path estimation, and thus the spatial resolution of an image.

Proton CT (pCT) nowadays aims at improving hadron therapy treatment planning by mapping the relative stopping power (RSP) of materials with respect to water. The RSP depends mainly on the electron density of the materials. The main information used is the energy of the protons. However, during a pCT acquisition, the spatial and angular deviation of each particle is recorded and the information about its transmission is implicitly available. The potential use of those observables in order to get information about the materials is being investigated. Monte Carlo simulations of protons sent into homogeneous materials were performed, and the influence of the chemical composition on the outputs was studied. A pCT acquisition of a head phantom scan was simulated. Brain lesions with the same electron density but different concentrations of oxygen were used to evaluate the different observables. Tomographic images from the different physics processes were reconstructed using a filtered back-projection algorithm. Preliminary results indicate that information is present in the reconstructed images of transmission and angular deviation that may help differentiate tissues. However, the statistical uncertainty on these observables generates further challenge in order to obtain an optimal reconstruction and extract the most pertinent information.

The PB approach to proton imaging may allow technical challenges imposed by the current proton-by-proton method to be overcome. In this framework, an analytical algorithm is proposed. Further work will involve a detailed study of the performances and limitations of this approach in terms of image quality. The paper shows how to account for the MCS in the reconstruction step with this new approach when an analytical reconstruction algorithm is used.

Proton imaging is developed in order to improve the accuracy of charged particle therapy treatment planning. It makes it possible to directly map the relative stopping powers of the materials using the information on the energy loss of the protons. In order to reach a satisfactory spatial resolution in the reconstructed images, the position and direction of each particle is recorded upstream and downstream from the patient. As a consequence of individual proton detection, information on the transmission rate and scattering of the protons is available. Image reconstruction processes are proposed to make use of this information. A proton tomographic acquisition of an anthropomorphic head phantom was simulated. The transmission rate of the particles was used to reconstruct a map of the macroscopic cross section for nuclear interactions of the materials. A two-step iterative reconstruction process was implemented to reconstruct a map of the inverse scattering length of the materials using the scattering of the protons. Results indicate that, while the reconstruction processes should be optimized, it is possible to extract quantitative information from the transmission rate and scattering of the protons. This suggests that proton imaging could provide additional knowledge on the materials that may be of use to further improve treatment planning.

No abstract