A comparison of proton stopping power measured with proton CT and x-ray CT in fresh postmortem porcine structures.

PURPOSE: Currently, calculations of proton range in proton therapy patients are based on a conversion of CT Hounsfield units of patient tissues into proton relative stopping power. Uncertainties in this conversion necessitate larger proximal and distal planned target volume margins. Proton CT can potentially reduce these uncertainties by directly measuring proton stopping power. We aim to demonstrate proton CT imaging with complex porcine samples, to analyze in detail three-dimensional regions of interest, and to compare proton stopping powers directly measured by proton CT to those determined from x-ray CT scans. METHODS: We have used a prototype proton imaging system with single proton tracking to acquire proton radiography and proton CT images of a sample of porcine pectoral girdle and ribs, and a pig's head. We also acquired close in time x-ray CT scans of the same samples and compared proton stopping power measurements from the two modalities. In the case of the pig's head, we obtained x-ray CT scans from two different scanners and compared results from high-dose and low-dose settings. RESULTS: Comparing our reconstructed proton CT images with images derived from x-ray CT scans, we find agreement within 1% to 2% for soft tissues and discrepancies of up to 6% for compact bone. We also observed large discrepancies, up to 40%, for cavitated regions with mixed content of air, soft tissue, and bone, such as sinus cavities or tympanic bullae. CONCLUSIONS: Our images and findings from a clinically realistic proton CT scanner demonstrate the potential for proton CT to be used for low-dose treatment planning with reduced margins.

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

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.

Analysis of characteristics of images acquired with a prototype clinical proton radiography system.

PURPOSE: Verification of patient-specific proton stopping powers obtained in the patient's treatment position can be used to reduce the distal and proximal margins needed in particle beam planning. Proton radiography can be used as a pretreatment instrument to verify integrated stopping power consistency with the treatment planning CT. Although a proton radiograph is a pixel by pixel representation of integrated stopping powers, the image may also be of high enough quality and contrast to be used for patient alignment. This investigation quantifies the accuracy and image quality of a prototype proton radiography system on a clinical proton delivery system. METHODS: We have developed a clinical prototype proton radiography system designed for integration into efficient clinical workflows. We tested the images obtained by this system for water-equivalent thickness (WET) accuracy, image noise, and spatial resolution. We evaluated the WET accuracy by comparing the average WET and rms error in several regions of interest (ROI) on a proton radiograph of a custom peg phantom. We measured the spatial resolution on a CATPHAN Line Pair phantom and a custom edge phantom by measuring the 10% value of the modulation transfer function (MTF). In addition, we tested the ability to detect proton range errors due to anatomical changes in a patient with a customized CIRS pediatric head phantom and inserts of varying WET placed in the posterior fossae of the brain. We took proton radiographs of the phantom with each insert in place and created difference maps between the resulting images. Integrated proton range was measured from an ROI in the difference maps. RESULTS: We measured the WET accuracy of the proton radiographic images to be ±0.2 mm (0.33%) from known values. The spatial resolution of the images was 0.6 lp/mm on the line pair phantom and 1.13 lp/mm on the edge phantom. We were able to detect anatomical changes producing changes in WET as low as 0.6 mm. CONCLUSION: The proton radiography system produces images with image quality sufficient for pretreatment range consistency verification.

Technical Note: A fast and monolithic prototype clinical proton radiography system optimized for pencil beam scanning.

PURPOSE: To demonstrate a proton-imaging system based on well-established fast scintillator technology to achieve high performance with low cost and complexity, with the potential of a straightforward translation into clinical use. METHODS: The system tracks individual protons through one (X, Y) scintillating fiber tracker plane upstream and downstream of the object and into a 13-cm -thick scintillating block residual energy detector. The fibers in the tracker planes are multiplexed into silicon photomultipliers (SiPMs) to reduce the number of electronics channels. The light signal from the residual energy detector is collected by 16 photomultiplier tubes (PMTs). Only four signals from the PMTs are output from each event, which allows for fast signal readout. A robust calibration method of the PMT signal to residual energy has been developed to obtain accurate proton images. The development of patient-specific scan patterns using multiple input energies allows for an image to be produced with minimal excess dose delivered to the patient. RESULTS: The calibration of signals in the energy detector produces accurate residual range measurements limited by intrinsic range straggling. We measured the water-equivalent thickness (WET) of a block of solid water (physical thickness of 6.10 mm) with a proton radiograph. The mean WET from all pixels in the block was 6.13 cm (SD 0.02 cm). The use of patient-specific scan patterns using multiple input energies enables imaging with a compact range detector. CONCLUSIONS: We have developed a prototype clinical proton radiography system for pretreatment imaging in proton radiation therapy. We have optimized the system for use with pencil beam scanning systems and have achieved a reduction of size and complexity compared to previous designs.

Accuracy of low-dose proton CT image registration for pretreatment alignment verification in reference to planning proton CT.

PURPOSE: Proton CT (pCT) has the ability to reduce inherent uncertainties in proton treatment by directly measuring the relative proton stopping power with respect to water, thereby avoiding the uncertain conversion of X-ray CT Hounsfield unit to relative stopping power and the deleterious effect of X- ray CT artifacts. The purpose of this work was to further evaluate the potential of pCT for pretreatment positioning using experimental pCT data of a head phantom. METHODS: The performance of a 3D image registration algorithm was tested with pCT reconstructions of a pediatric head phantom. A planning pCT simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time. The pretreatment pCT scan was rigidly transformed by prescribing random errors with six degrees of freedom or deformed by the deformation field derived from a head and neck cancer patient to the pretreatment pCT reconstruction, respectively. After applying the rigid or deformable image registration algorithm to retrieve the original pCT image before transformation, the accuracy of the registration was assessed. To simulate very low-dose imaging for patient setup, the proton CT images were reconstructed with 100%, 50%, 25%, and 12.5% of the total number of histories of the original planning pCT simulation scan, respectively. RESULTS: The residual errors in image registration were lower than 1 mm and 1° of magnitude regardless of the anatomic directions and imaging dose. The mean residual errors ranges found for rigid image registration were from -0.29 ± 0.09 to 0.51 ± 0.50 mm for translations and from -0.05 ± 0.13 to 0.08 ± 0.08 degrees for rotations. The percentages of sub-millimetric errors found, for deformable image registration, were between 63.5% and 100%. CONCLUSION: This experimental head phantom study demonstrated the potential of low-dose pCT imaging for 3D image registration. Further work is needed to confirm the value pCT for pretreatment image-guided proton therapy.

Reconstructed and Real Proton Radiographs for Image-Guidance in Proton Beam Therapy.

One of the major challenges to proton beam therapy at this time is the uncertainty of the true range of a clinical treatment proton beam as it traverses the various tissues and organs in a human body. This uncertainty necessitates the addition of greater "margins" to the planning target volume along the direction of the beam to ensure safety and tumor target coverage. Proton radiography holds promise as both an image-guidance method for proton beam therapy and as a means of estimating particle beam range in the clinic. In this brief report, we present some of the first real and reconstructed proton radiographs using our particular system. Our qualitative review of these images indicates that this method has excellent potential as a proton radiography-based image guidance system. Based on the encouraging results of our preliminary work, more rigorous and quantitative analyses will be performed shortly and we shall continue to explore the potential of this approach for addressing the particle beam range uncertainty issue.

Fast In Situ Image Reconstruction for Proton Radiography.

OBJECTIVE: Proton beam therapy is an emerging modality for cancer treatment that, compared to X-ray radiation therapy, promises to provide better dose delivery to clinical targets with lower doses to normal tissues. Crucial to accurate treatment planning and dose delivery is knowledge of the water equivalent path length (WEPL) of each ray, or pencil beam, from the skin to every point in the target. For protons, this length is estimated from relative stopping power based on X-ray Hounsfield units. Unfortunately, such estimates lead to 3 to 4% uncertainties in the proton range prediction. Therefore, protons in the Bragg peak may overshoot (or undershoot) the desired stopping depth in the target causing tissue damage beyond the target volume. Recent studies indicate that tomographic imaging using protons has the potential to provide directly more accurate measurement of RSPs with significantly lower radiation dose than X-rays. We are currently working on a proton radiography system that promises to provide accurate two-dimensional (2D) images of WEPL values for protons that pass through the body. These will be suitable for positioning and range verification in daily treatments. In this study, we demonstrate that this system is capable of rapidly achieving such accurate images in clinically meaningful times. METHODS: We have developed a software platform to characterize the potential performance of the prototype proton radiography system. We use Geant4 to simulate raw data detected by the device. An especially-written software - pRad - was written to process these data as they are received and uses iterative methods to generate radiographs. The software has been designed to generate a radiograph from a few million protons in under a minute after receiving the first proton from the device. We used a head phantom with known chemical compositions that could be modelled quite accurately in Geant4 simulations of proton radiographs. The radiographs are displayed as pixelated WEPL values displayed on a 2D gray scale image of WEPL values. RESULTS: Rapid radiograph reconstruction of 3D phantoms using simulated proton pencil beams have been achieved with our software platform. On a modest desktop computer with a single central processing unit (CPU) and a single graphics processing unit (GPU), it takes about 11 seconds to reconstruct images using iterative linear algorithms to reconstruct a radiograph from 7.6 million protons. For the radiographic reconstructions of the head phantom described here, the mean WEPL errors, in the proton radiograph using a large majority of the pixels in the complete image were less than 1 mm when compared to images obtained without proton scattering and without detector resolution included. CONCLUSION: We have demonstrated, through computer simulations of proton irradiation of a pediatric head phantom using the newly built pRad detector and image reconstruction software, that high quality proton radiographs can be generated for patient alignment and verification of water equivalent thickness of the patient before each treatment.

The geometric response function for convergent slit-slat collimators.

We have derived an analytic geometric transfer function (GTF) for a convergent slit-slat collimator that treats the parallel slit-slat collimator as a special case. The effective point spread function (EPSF) is then derived from the GTF through the Fourier transform. The results of these derivations give an accurate description of the complete geometric response for a slit-slat collimator that includes the effects of the shape and orientation of the slit and slats. We have also derived exact and approximate sensitivity formulae and spatial resolution formulae using the EPSF.

C-SPECT - a Clinical Cardiac SPECT/Tct Platform: Design Concepts and Performance Potential.

Because of scarcity of photons emitted from the heart, clinical cardiac SPECT imaging is mainly limited by photon statistics. The sub-optimal detection efficiency of current SPECT systems not only limits the quality of clinical cardiac SPECT imaging but also makes more advanced potential applications difficult to be realized. We propose a high-performance system platform - C-SPECT, which has its sampling geometry optimized for detection of emitted photons in quality and quantity. The C-SPECT has a stationary C-shaped gantry that surrounds the left-front side of a patient's thorax. The stationary C-shaped collimator and detector systems in the gantry provide effective and efficient detection and sampling of photon emission. For cardiac imaging, the C-SPECT platform could achieve 2 to 4 times the system geometric efficiency of conventional SPECT systems at the same sampling resolution. This platform also includes an integrated transmission CT for attenuation correction. The ability of C-SPECT systems to perform sequential high-quality emission and transmission imaging could bring cost-effective high-performance to clinical imaging. In addition, a C-SPECT system could provide high detection efficiency to accommodate fast acquisition rate for gated and dynamic cardiac imaging. This paper describes the design concepts and performance potential of C-SPECT, and illustrates how these concepts can be implemented in a basic system.