High-Density Glass Scintillators for Proton Radiography-Relative Luminosity, Proton Response, and Spatial Resolution.

Proton radiography is a promising development in proton therapy, and researchers are currently exploring optimal detector materials to construct proton radiography detector arrays. High-density glass scintillators may improve integrating-mode proton radiography detectors by increasing spatial resolution and decreasing detector thickness. We evaluated several new scintillators, activated with europium or terbium, with proton response measurements and Monte Carlo simulations, characterizing relative luminosity, ionization quenching, and proton radiograph spatial resolution. We applied a correction based on Birks's analytical model for ionization quenching. The data demonstrate increased relative luminosity with increased activation element concentration, and higher relative luminosity for samples activated with europium. An increased glass density enables more compact detector geometries and higher spatial resolution. These findings suggest that a tungsten and gadolinium oxide-based glass activated with 4% europium is an ideal scintillator for testing in a full-size proton radiography detector.

Whole-beam self-focusing in fusion-relevant plasma.

Fast ignition inertial confinement fusion requires the production of a low-density channel in plasma with density scale-lengths of several hundred microns. The channel assists in the propagation of an ultra-intense laser pulse used to generate fast electrons which form a hot spot on the side of pre-compressed fusion fuel. We present a systematic characterization of an expanding laser-produced plasma using optical interferometry, benchmarked against three-dimensional hydrodynamic simulations. Magnetic fields associated with channel formation are probed using proton radiography, and compared to magnetic field structures generated in full-scale particle-in-cell simulations. We present observations of long-lived, straight channels produced by the Habara-Kodama-Tanaka whole-beam self-focusing mechanism, overcoming a critical barrier on the path to realizing fast ignition. This article is part of a discussion meeting issue 'Prospects for high gain inertial fusion energy (part 2)'.

A denoising method based on deep learning for proton radiograph using energy resolved dose function.

Objective.Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the change of water equivalent path length (WEPL) resulting in the lower WEPL measurement accuracy. In this study, we proposed a new denoising method of proton radiographs based on energy resolved dose function curves.Approach.Firstly, the corresponding relationship between the distortion of WEPL characteristic curve, and energy and proportion of scattered protons was established. Then, to improve the accuracy of proton radiographs, deep learning technique was used to remove scattered protons and correct deviated WEPL values. Experiments on a calibration phantom to prove the effectiveness and feasibility of this method were performed. In addition, an anthropomorphic head phantom was selected to demonstrate the clinical relevance of this technology and the denoising effect was analyzed.Main results.The curves of WEPL profiles of proton radiographs became smoother and deviated WEPL values were corrected. For the calibration phantom proton radiograph, the average absolute error of WEPL values decreased from 2.23 to 1.72, the mean percentage difference of all materials of relative stopping power decreased from 1.24 to 0.39, and the average relative WEPL corrected due to the denoising process was 1.06%. In addition, WEPL values correcting were also observed on the proton radiograph for anthropomorphic head phantom due to this denoising process.Significance.The experiments showed that this new method was effective for proton radiograph denoising and had greater advantages than end-to-end image denoising methods, laying the foundation for the implementation of precise proton radiotherapy.

Technical note: development of a simulation framework, enabling the investigation of locally tuned single energy proton radiography.

Range uncertainties remain a limitation for the confined dose distribution that proton therapy can offer. The uncertainty stems from the ambiguity when translating CT Hounsfield Units (HU) into proton stopping powers. Proton Radiography (PR) can be used to verify the proton range. Specifically, PR can be used as a quality-control tool for CBCT-based synthetic CTs. An essential part of the work illustrating the potential of PR has been conducted using multi-layer ionization chamber (MLIC) detectors and mono-energetic PR. Due to the dimensions of commercially available MLICs, clinical adoption is cumbersome. Here, we present a simulation framework exploring locally-tuned single energy (LTSE) proton radiography and corresponding potential compact PR detector designs. Based on a planning CT data set, the presented framework models the water equivalent thickness. Subsequently, it analyses the proton energies required to pass through the geometry within a defined ROI. In the final step, an LTSE PR is simulated using the MCsquare Monte Carlo code. In an anatomical head phantom, we illustrate that LTSE PR allows for a significantly shorter longitudinal dimension of MLICs. We compared PR simulations for two exemplary 30 × 30 mm2proton fields passing the phantom at a 90° angle at an anterior and a posterior location in an iso-centric setup. The longitudinal distance over which all spots per field range out is significantly reduced for LTSE PR compared to mono-energetic PR. In addition, we illustrate the difference in shape of integral depth dose (IDD) when using constrained PR energies. Finally, we demonstrate the accordance of simulated and experimentally acquired IDDs for an LTSE PR acquisition. As the next steps, the framework will be used to investigate the sensitivity of LTSE PR to various sources of errors. Furthermore, we will use the framework to systematically explore the dimensions of an optimized MLIC design for daily clinical use.

Integrated-mode proton radiography with 2D lateral projections.

Integrated-mode proton radiography leading to water equivalent thickness (WET) maps is an avenue of interest for motion management, patient positioning, andin vivorange verification. Radiographs can be obtained using a pencil beam scanning setup with a large 3D monolithic scintillator coupled with optical cameras. Established reconstruction methods either (1) involve a camera at the distal end of the scintillator, or (2) use a lateral view camera as a range telescope. Both approaches lead to limited image quality. The purpose of this work is to propose a third, novel reconstruction framework that exploits the 2D information provided by two lateral view cameras, to improve image quality achievable using lateral views. The three methods are first compared in a simulated Geant4 Monte Carlo framework using an extended cardiac torso (XCAT) phantom and a slanted edge. The proposed method with 2D lateral views is also compared with the range telescope approach using experimental data acquired with a plastic volumetric scintillator. Scanned phantoms include a Las Vegas (contrast), 9 tissue-substitute inserts (WET accuracy), and a paediatric head phantom. Resolution increases from 0.24 (distal) to 0.33 lp mm-1(proposed method) on the simulated slanted edge phantom, and the mean absolute error on WET maps of the XCAT phantom is reduced from 3.4 to 2.7 mm with the same methods. Experimental data from the proposed 2D lateral views indicate a 36% increase in contrast relative to the range telescope method. High WET accuracy is obtained, with a mean absolute error of 0.4 mm over 9 inserts. Results are presented for various pencil beam spacing ranging from 2 to 6 mm. This work illustrates that high quality proton radiographs can be obtained with clinical beam settings and the proposed reconstruction framework with 2D lateral views, with potential applications in adaptive proton therapy.

A TOPAS model for lens-based proton radiography.

Objective.Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging.Approach.The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230-930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according toßγ,the inverse product of particle relativistic velocity and particle momentum.Main results.Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV.Significance.Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.

A comparative study of machine-learning approaches in proton radiography using energy-resolved dose function.

PURPOSE: The feasibility of machine learning (ML) techniques and their performance compared to the conventional χ2-minimization technique in the context of the proton energy-resolved dose imaging method are presented. MATERIALS AND METHOD: Various geometries resembling a wedge and varying gradients are simulated in GATE to obtain energy-resolved dose functions (ERDF) from proton beams of different energies. These ERDFs are used to predict the WEPL using a conventional technique and other ML-based methods. The results are compared to gain an understanding of the performance of ML models in proton radiography. RESULTS: The results obtained from the χ2-minimization technique indicate that it is robust and more reliable compared to the ML-based techniques. It is also observed that the ML-based techniques did not mitigate the effect of range-mixing but seem to be more affected by it compared to the χ2-minimization technique. Substantial data reduction was required in order to make the results of ML-based methods comparable to that of χ2-minimization. We also note that such data reduction might not be possible in a clinical setting. The only advantage in using the ML-based technique is the computational time required to generate a WEPL map which, in our case study, is 10-30 times shorter than the time required for the conventional χ2-minimization technique. CONCLUSIONS: The first results from this preliminary study indicate that the ML techniques failed to be on par with the conventional χ2-minimization technique in terms of the achievable accuracy in the predictions of WEPL and in the mitigation of range-mixing effects in the WEPL image. Modern strategies like the GAN-based models may be suitable for such applications.

A Monte-Carlo-based study of a single-2D-detector proton-radiography system.

PURPOSE: To assess the feasibility of a proton radiography (pRG) system based on a single thin pixelated detector for water-equivalent path length (WEPL) and relative stopping power (RSP) measurements. METHODS: A model of a pRG system consisting of a single pixelated detector measuring energy deposition and proton fluence was investigated in a Geant4-based Monte Carlo study. At the position directly after an object traversed by a broad proton beam, spatial 2D distributions are calculated of the energy deposition in, and the number of protons entering the detector. Their ratio relates to the 2D distribution of the average stopping power of protons in the detector. The system response is calibrated against the residual range in water of the protons to provide the 2D distribution of the WEPL of the object. The WEPL distribution is converted into the distribution of the RSP of the object. Simulations have been done, where the system has been tested on 13 samples of homogeneous materials of which the RSPs have been calculated and compared with RSPs determined from simulations of residual-range-in-water, which we refer to as reference RSPs. RESULTS: For both human-tissue- and non-human-tissue-equivalent materials, the RSPs derived with the detector agree with the reference values within 1%. CONCLUSION: The study shows that a pRG system based on one thin pixelated detection screen has the potential to provide RSP predictions with an accuracy of 1%.

Extension of the open-source TIGRE toolbox for proton imaging.

Proton irradiation is a well-established method to treat deep-seated tumors in radio oncology. Usually, an X-ray computed tomography (CT) scan is used for treatment planning. Since proton therapy is based on the precise knowledge of the stopping power describing the energy loss of protons in the patient tissues, the Hounsfield units of the planning CT have to be converted. This conversion introduces range errors in the treatment plan, which could be reduced, if the stopping power values were extracted directly from an image obtained using protons instead of X-rays. Since protons are affected by multiple Coulomb scattering, reconstruction of the 3D stopping power map results in limited image quality if the curved proton path is not considered. This work presents a substantial code extension of the open-source toolbox TIGRE for proton CT (pCT) image reconstruction based on proton radiographs including a curved proton path estimate. The code extension and the reconstruction algorithms are GPU-based, allowing to achieve reconstruction results within minutes. The performance of the pCT code extension was tested with Monte Carlo simulated data using three phantoms (Catphan® high resolution and sensitometry modules and a CIRS patient phantom). In the simulations, ideal and non-ideal conditions for a pCT setup were assumed. The obtained mean absolute percentage error was found to be below 1% and up to 8 lp/cm could be resolved using an idealized setup. These findings demonstrate that the presented code extension to the TIGRE toolbox offers the possibility for other research groups to use a fast and accurate open-source pCT reconstruction.

Technical note: Flat panel proton radiography with a patient specific imaging field for accurate WEPL assessment.

BACKGROUND: Proton radiography (PR) uses highly energetic proton beams to create images where energy loss is the main contrast mechanism. Water-equivalent path length (WEPL) measurements using flat panel PR (FP-PR) have potential for in vivo range verification. However, an accurate WEPL measurement via FP-PR requires irradiation with multiple energy layers, imposing high imaging doses. PURPOSE: A FP-PR method is proposed for accurate WEPL determination based on a patient-specific imaging field with a reduced number of energies (n) to minimize imaging dose. METHODS: Patient-specific FP-PRs were simulated and measured for a head and neck (HN) phantom. An energy selection algorithm estimated spot-wise the lowest energy required to cross the anatomy (Emin) using a water-equivalent thickness map. Starting from Emin, n was restricted to certain values (n = 26, 24, 22, …, 2 for simulations, n = 10 for measurements), resulting in patient-specific FP-PRs. A reference FP-PR with a complete set of energies was compared against patient-specific FP-PRs covering the whole anatomy via mean absolute WEPL differences (MAD), to evaluate the impact of the developed algorithm. WEPL accuracy of patient-specific FP-PRs was assessed using mean relative WEPL errors (MRE) with respect to measured multi-layer ionization chamber PRs (MLIC-PR) in the base of skull, brain, and neck regions. RESULTS: MADs ranged from 2.1 mm (n = 26) to 21.0 mm (n = 2) for simulated FP-PRs, and 7.2 mm for measured FP-PRs (n = 10). WEPL differences below 1 mm were observed across the whole anatomy, except at the phantom surfaces. Measured patient-specific FP-PRs showed good agreement against MLIC-PRs, with MREs of 1.3 ± 2.0%, -0.1 ± 1.0%, and -0.1 ± 0.4% in the three regions of the phantom. CONCLUSION: A method to obtain accurate WEPL measurements using FP-PR with a reduced number of energies selected for the individual patient anatomy was established in silico and validated experimentally. Patient-specific FP-PRs could provide means of in vivo range verification.

A novel proton-integrating radiography system design using a monolithic scintillator detector: experimental studies.

Research on proton-based imaging systems aims to improve treatment planning, internal anatomy visualization, and patient alignment for proton radiotherapy. The purpose of this study was to demonstrate a new proton radiography system design consisting of a monolithic plastic scintillator volume and two optical cameras for use with scanning proton pencil beams. Unlike the thin scintillating plates currently used for proton radiography, the plastic scintillator volume (20 × 20 × 20 cm3) captures a wider distribution of proton beam energy depositions and avoids proton-beam modulation. The proton imaging system's characteristics were tested using image uniformity (2.6% over a 5 × 5 cm2 area), stability (0.37%), and linearity (R2 = 1) studies. We used the light distribution produced within the plastic scintillator to generate proton radiographs via two different approaches: (a) integrating light by using a camera placed along the beam axis, and (b) capturing changes to the proton Bragg peak positions with a camera placed perpendicularly to the beam axis. The latter method was used to plot and evaluate relative shifts in percentage depth light (PDL) profiles of proton beams with and without a phantom in the beam path. A curvelet minimization algorithm used differences in PDL profiles to reconstruct and refine the phantom water-equivalent thickness (WET) map. Gammex phantoms were used to compare the proton radiographs generated by these two methods. The relative accuracies in calculating WET of the phantoms using the calibration-based beam-integration (and the PDL) methods were -0.18 ± 0.35% (-0.29 ± 3.11%), -0.11 ± 0.51% (-0.15 ± 2.64%), -2.94 ± 1.20% (-0.75 ± 6.11%), and -1.65 ± 0.35% (0.36 ± 3.93%) for solid water, adipose, cortical bone, and PMMA, respectively. Further exploration of this unique multicamera-based imaging system is warranted and could lead to clinical applications that improve treatment planning and patient alignment for proton radiotherapy.

Investigation of the effect of air gap size on the spatial resolution in proton- and helium radio- and tomography.

PURPOSE: Proton computed (transmission) tomography (pCT) refers to the process of imaging an object by letting protons pass through it, while measuring their energy after, and their position and (optionally) direction both before and after their traversal through that object. The so far experimental technique has potential to improve treatment planning of proton therapy by enabling the direct acquisition of a proton stopping power map of tissue, thus removing the need to obtain it by converting X-ray CT attenuation data and thereby eliminating uncertainties which arise in the mentioned conversion process. The image reconstruction in pCT requires accurate estimates of the proton trajectories. In experimental pCT detector setups where the direction of the protons is not measured, the air gap between the detector planes and the imaged object worsens the spatial resolution of the image obtained. In this work we determined the mean proton paths and the corresponding spatial uncertainty, taking into account the presence of the air gap. METHODS: We used Monte Carlo simulations of radiation transport to systematically investigate the effect of the air gap size between detector and patient on the spatial resolution of proton (ion) computed tomography for protons with an energy of 200MeV and 250MeV as well as for helium ions (He-4) with an energy of 798MeV. For the simulations we used TOPAS which itself is based on Geant4. RESULTS: For all particles, which are detected at the same entrance and exit coordinate, the average ion path and the corresponding standard deviation was computed. From this information, the dependence of the spatial resolution on the air gap size and the angular confusion of the particle beam was inferred. CONCLUSION: The presence of the airgap does not pose a problem for perfect fan beams. In realistic scenarios, where the initial angular confusion is around 5mrad and for typical air gap sizes up to 10cm, using an energy of 200MeV a spatial resolution of about 1.6mm can be achieved. Using protons with E=250MeV a spatial resolution of about 1.1mm and using helium ions (He-4) with E=798MeV even a spatial resolution below 0.7mm respectively is attainable.

Combined proton radiography and irradiation for high-precision preclinical studies in small animals.

Background and purpose: Proton therapy has become a popular treatment modality in the field of radiooncology due to higher spatial dose conformity compared to conventional radiotherapy, which holds the potential to spare normal tissue. Nevertheless, unresolved research questions, such as the much debated relative biological effectiveness (RBE) of protons, call for preclinical research, especially regarding in vivo studies. To mimic clinical workflows, high-precision small animal irradiation setups with image-guidance are needed. Material and methods: A preclinical experimental setup for small animal brain irradiation using proton radiographies was established to perform planning, repositioning, and irradiation of mice. The image quality of proton radiographies was optimized regarding the resolution, contrast-to-noise ratio (CNR), and minimal dose deposition in the animal. Subsequently, proof-of-concept histological analysis was conducted by staining for DNA double-strand breaks that were then correlated to the delivered dose. Results: The developed setup and workflow allow precise brain irradiation with a lateral target positioning accuracy of<0.26mm for in vivo experiments at minimal imaging dose of<23mGy per mouse. The custom-made software for image registration enables the fast and precise animal positioning at the beam with low observer-variability. DNA damage staining validated the successful positioning and irradiation of the mouse hippocampus. Conclusion: Proton radiography enables fast and effective high-precision lateral alignment of proton beam and target volume in mouse irradiation experiments with limited dose exposure. In the future, this will enable irradiation of larger animal cohorts as well as fractionated proton irradiation.

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.

Artificial intelligence supported single detector multi-energy proton radiography system.

Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).

Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients.

PURPOSE: Cone-beam CT (CBCT)-based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range-probing (RP)-based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs. METHODS: A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT-based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. RESULTS: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from -1.2% to 1.5% in rCTs, and from -0.7% to 2.7% in sCTs. CONCLUSIONS: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Contrast-enhanced proton radiographic sensitivity limits for tumor detection.

Purpose: Proton radiography may guide proton therapy cancer treatments with beam's-eye-view anatomical images and a proton-based estimation of proton stopping power. However, without contrast enhancement, proton radiography will not be able to distinguish tumor from tissue. To provide this contrast, functionalized, high- Z nanoparticles that specifically target a tumor could be injected into a patient before imaging. We conducted this study to understand the ability of gold, as a high- Z , biologically compatible tracer, to differentiate tumors from surrounding tissue. Approach: Acrylic and gold phantoms simulate a tumor tagged with gold nanoparticles (AuNPs). Calculations correlate a given thickness of gold to levels of tumor AuNP uptake reported in the literature. An identity, × 3 , and × 7 proton magnifying lens acquired lens-refocused proton radiographs at the 800-MeV LANSCE proton beam. The effects of gold in the phantoms, in terms of percent density change, were observed as changes in measured transmission. Variable areal densities of acrylic modeled the thickness of the human body. Results: A 1 - µ m -thick gold strip was discernible within 1 cm of acrylic, an areal density change of 0.2%. Behind 20 cm of acrylic, a 40 - µ m gold strip was visible. A 1-cm-diameter tumor tagged with 1 × 10 5 50-nm AuNPs per cell has an amount of contrast agent embedded within it that is equivalent to a 65 - µ m thickness of gold, an areal density change of 0.63% in a tissue thickness of 20 cm, which is expected to be visible in a typical proton radiograph. Conclusions: We indicate that AuNP-enhanced proton radiography might be a feasible technology to provide image-guidance to proton therapy, potentially reducing off-target effects and sparing nearby tissue. These data can be used to develop treatment plans and clinical applications can be derived from the simulations.

First experimental results of gated proton imaging using x-ray fluoroscopy to detect a fiducial marker.

Increasing numbers of proton imaging research studies are being conducted for accurate proton range determination in proton therapy treatment planning. However, there is no proton imaging system that deals with motion artifacts. In this study, a gated proton imaging system was developed and the first experimental results of proton radiography (pRG) were obtained for a moving object without motion artifacts. A motion management system using dual x-ray fluoroscopy for detecting a spherical gold fiducial marker was introduced and the proton beam was gated in accordance with the motion of the object. To demonstrate the performance of the gated proton imaging system, gated pRG images of a moving phantom were acquired experimentally, and the motion artifacts clearly were diminished. Also, the factors causing image deteriorations were evaluated focusing on the new gating system developed here, and the main factor was identified as the latency (with a maximum value of 93 ms) between the ideal gating signal according to the actual marker position and the actual gating signal. The possible deterioration due to the latency of the proton imaging system and proton beam irradiation was small owing to appropriate setting of the time structure.

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: First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients.

PURPOSE: The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting. METHODS AND MATERIALS: This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocenter were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented. RESULTS: Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation). CONCLUSIONS: The results of an RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy.