[en] This work investigates the attenuation of a proton beam to reconstruct the map of the linear attenuation coefficient of a material which is mainly caused by the inelastic interactions of protons with matter. Attenuation proton computed tomography (pCT) suffers from a poor spatial resolution due to multiple Coulomb scattering (MCS) of protons in matter, similarly to the conventional energy-loss pCT. We therefore adapted a recent filtered back-projection algorithm along the most likely path (MLP) of protons for energy-loss pCT (Rit et al 2013) to attenuation pCT assuming a pCT scanner that can track the position and the direction of protons before and after the scanned object. Monte Carlo simulations of pCT acquisitions of density and spatial resolution phantoms were performed to characterize the new algorithm using Geant4 (via Gate). Attenuation pCT assumes an energy-independent inelastic cross-section, and the impact of the energy dependence of the inelastic cross-section below 100 MeV showed a capping artifact when the residual energy was below 100 MeV behind the object. The statistical limitation has been determined analytically and it was found that the noise in attenuation pCT images is 411 times and 278 times higher than the noise in energy-loss pCT images for the same imaging dose at 200 MeV and 300 MeV, respectively. Comparison of the spatial resolution of attenuation pCT images with a conventional straight-line path binning showed that incorporating the MLP estimates during reconstruction improves the spatial resolution of attenuation pCT. Moreover, regardless of the significant noise in attenuation pCT images, the spatial resolution of attenuation pCT was better than that of conventional energy-loss pCT in some studied situations thanks to the interplay of MCS and attenuation known as the West–Sherwood effect. (paper)

[en] Proton computed tomography (CT) is an imaging modality investigated mainly in the context of proton therapy as a complement to x-ray CT. It uses protons with high enough energy to fully traverse the imaged object. Common prototype systems measure each proton’s position and direction upstream and downstream of the object as well as the energy loss which can be converted into the water equivalent thickness. A reconstruction algorithm then produces a map of the relative stopping power in the object. As an alternative to energy-loss proton CT, it has been proposed to reconstruct a map of the object’s scattering power based on the protons’ angular dispersion which can be estimated from the measured directions. As in energy-loss proton CT, reconstruction should best be performed considering the non-linear shape of proton trajectories due to multiple Coulomb scattering (MCS), but no algorithm to achieve this is so far available in the literature.

In this work, we propose a filtered backprojection algorithm with distance-driven binning to account for the protons’ most likely path. Furthermore, we present a systematic study of scattering proton CT in terms of inherent noise and spatial resolution and study the artefacts which arise from the physics of MCS. Our analysis is partly based on analytical models and partly on Monte Carlo simulations.

Our results show that the proposed algorithm performs well in reconstructing relative scattering power maps, i.e. scattering power relative to that of water. Spatial resolution is improved by almost a factor of three compared to straight line projection and is comparable to energy-loss proton CT. Image noise, on the other hand, is inherently much higher. For example, in a water cylinder of 20 cm diameter, representative of a human head, noise in the central image pixel is about 40 times higher in scattering proton CT than in energy-loss proton CT. Relative scattering power in dense regions such as bone inserts is systematically underestimated by a few percent, depending on beam energy and phantom geometry. (paper)

[en] Proton computed tomography (CT) has been described as a solution for imaging the proton stopping power of patient tissues, therefore reducing the uncertainty of the conversion of x-ray CT images to relative stopping power (RSP) maps and its associated margins. This study aimed to investigate this assertion under the assumption of ideal detection systems. We have developed a Monte Carlo framework to assess proton CT performances for the main steps of a proton therapy treatment planning, i.e. proton or x-ray CT imaging, conversion to RSP maps based on the calibration of a tissue phantom, and proton dose simulations. Irradiations of a computational phantom with pencil beams were simulated on various anatomical sites and the proton range was assessed on the reference, the proton CT-based and the x-ray CT-based material maps. Errors on the tissue’s RSP reconstructed from proton CT were found to be significantly smaller and less dependent on the tissue distribution. The imaging dose was also found to be much more uniform and conformal to the primary beam. The mean absolute deviation for range calculations based on x-ray CT varies from 0.18 to 2.01 mm depending on the localization, while it is smaller than 0.1 mm for proton CT. Under the assumption of a perfect detection system, proton range predictions based on proton CT are therefore both more accurate and more uniform than those based on x-ray CT. (paper)