Abstract
Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.
Highlights
The interest in the technological development of proton computed tomography, called Proton CT (pCT) has increased in recent years due to its potential to reduce the range uncertaintyThe associate editor coordinating the review of this manuscript and approving it for publication was Larbi Boubchir .problem in proton therapy [1], [2]
The range of proton beams in a given patient is associated with substantial uncertainties, including the conversion of Hounsfield units (HU) to relative stopping power (RSP) with respect to water, daily variation in the patient setup as well as in the distribution and composition of tissues
Schultze et al.: Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning when the latter is performed with Monte Carlo algorithms
Summary
The interest in the technological development of proton computed tomography, called pCT has increased in recent years due to its potential to reduce the range uncertainty. The tracking of single protons through the object generally leads to a lower dose and better image quality but has been perceived as an impractical solution requiring complex equipment The purpose of this contribution is to present our approach to pCT data acquisition, preprocessing, and preconditioning for image reconstruction using single proton tracking and water-equivalent path length (WEPL) measurements to demonstrate that this solution is practical and clinically feasible. The general way in which proton imaging data are acquired can be divided into two conceptually different approaches: (a) The particle tracking (or list) mode, which uses technology similar to the one presented in this work and allows one to use the information from individual protons or ions traversing the patient and (b) the integration mode, which uses established therapeutic beam delivery modes of lower intensity and measures an integrated beam current dependent on the water-equivalent thickness (WET) of proton or ion beams traversing the patient, typically with existing dosimetric equipment. The angle θ of the rotational platform at each event is inferred from the time of its trigger and the constant 1 RPM rate of the platform
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