[Show abstract][Hide abstract] ABSTRACT: Advantages of proton computed tomography (pCT) have been recognized in the past. However, the quality of a pCT image may be limited due to the stochastic nature of the proton path inside the object. In this work, we report a preliminary study on reconstruction of pCT image with improved path estimation. A set of Monte Carlo simulations was carried out with the GEANT4 program, and reconstructed by filtered backprojection method. Simulations with different density contrast settings were compared, and spatial resolution around 0.5 mm for the highest contrast phantom was achieved, which is comparable to that of X-ray CT image. Further improvement by utilizing the statistical properties of proton transport is expected and is under progress.
[Show abstract][Hide abstract] ABSTRACT: Proton computed tomography (CT) has important implications for both image-guided diagnosis and radiation therapy. For diagnosis, the fact that the patient dose committed by proton CT compares favorably with that delivered by traditional X-ray CT, for the same density resolution and contrast, may be exploited in dose-critical clinical settings. Proton CT is also the most appropriate imaging method to perform planning and verification of proton-based radiation treatment, since proton stopping power maps constructed by table-based transformation of X-ray CT images only render approximate stopping power estimates. In proton CT, sharp features become blurred by the phenomenon of multiple Coulomb scattering (MCS), resulting in a resolution of around 3 to 5 mm. Studies showed that the spatial resolution of proton radiography and CT can be improved to about 1-2 mm by tracking individual protons in coincidence as they enter and exit the imaged object. This paper describes a new practical implementation of this approach. We first bin the captured protons into slots of similar tracks. Optionally, proton energy statistics can be collected within each bin to obtain further parameters for tissue characterization. The envelope of path uncertainty due to MCS can be be modeled as a banana-shaped curve. The 3D reconstruction, using either filter-backprojection or iterative algorithms, can be performed rapidly on graphics hardware, using a slice blurring technique to model the MCS uncertainty curve.