Conference Paper

Design of a proton computed tomography system for applications in proton radiation therapy

Dept. of Radiat. Medicine, Loma Linda Univ. Med. Center, CA, USA
DOI: 10.1109/NSSMIC.2003.1352179 Conference: Nuclear Science Symposium Conference Record, 2003 IEEE, Volume: 3
Source: IEEE Xplore

ABSTRACT Proton computed tomography (pCT) has the potential to improve the accuracy of dose calculations for proton treatment planning, and will also be useful for pretreatment verification of patient positioning relative to the proton beam. A design study was performed to define the optimal approach to a pCT system based on specifications for applications in proton therapy. Conceptual and detailed design of a pCT system is presented consisting of a silicon-based particle tracking system and a crystal calorimeter to measure energy loss of individual protons. We discuss the formation of pCT images based on the reconstruction of volume electron density maps and the suitability of analytic and statistical algorithms for image reconstruction.

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    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.
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