Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning

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. INDEX TERMS Proton computed tomography, data acquisition, preprocessing, initial image formation I. INTRODUCTION T HE interest in the technological development of proton computed tomography, called pCT herein, has increased in recent years due to its potential to reduce the range uncertainty 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. These uncertainties

Experimental comparison of photon versus particle computed tomography to predict tissue relative stopping powers

“…Hence, we kept the data processing for pCT to the conventional procedure with the detector. 43 A more detailed technical investigation on the increased uncertainty for pCT compared to HeCT with this detector is planned.…”

“…This may be related to detector effects not considered in the simulation work which are not yet fully understood. Hence, we kept the data processing for pCT to the conventional procedure with the detector 43 . A more detailed technical investigation on the increased uncertainty for pCT compared to HeCT with this detector is planned.…”

“…Initial filters acting on the reconstructed track segments and the energy loss in the five‐stage energy detector were applied to particle data to remove nuclear interactions and particles that scattered outside the detector 43 . For the helium scans, a E‐E technique was used to identify and remove secondary fragments for helium ion imaging 28 .…”

“…Secondary 3𝜎 filters acting on the angular displacement (difference between incoming and outgoing particle angle) and WEPL of the particles were applied to remove remaining particle histories with unusual WEPL and those that underwent large angle scattering events. 43 For filtering, the particles were binned into a grid of 1 mm 2 pixels according to the midpoint position of a straight line path connecting their entrance and exit point of the reconstruction volume.…”

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

“…In the work-in-progress described next, we developed and investigated an algorithmic framework that enables the reconstruction of the reference image of a breathing patient assuming that the tissue motion can be described by accurate deformation vector fields, see, e.g., [45]. For ordinary (motion-free) pCT data acquisition, one uses a detector-fixed coordinate system for tracking discrete proton coordinates for MLP calculation and an object-fixed coordinate system for reconstructing the RSP; both coordinate systems are represented by a regularly spaced, threedimensional voxel grid [46]. When motion is present, the object-fixed voxel grid is only regular for the reference state.…”