Particle arc therapy: Status and potential.

There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook.

Deep learning-based voxel sampling for particle therapy treatment planning.

OBJECTIVE: Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach: Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12,500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results: On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance: The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies. .

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization.

Objective.We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection.Approach.Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features' radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient.Main results.For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm.Significance.Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

Exploration of differentiability in a proton computed tomography simulation framework.

Objective.Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications.Approach.This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques.Main results.The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem.Significance.Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter.

Objective.Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots.Approach.We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction.Main results.The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm.Significance.Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.

Evaluation of proton and carbon ion beam models in TReatment Planning for Particles 4D (TRiP4D) referring to a commercial treatment planning system.

PURPOSE: To investigate the accuracy of the treatment planning system (TPS) TRiP4D in reproducing doses computed by the clinically used TPS SyngoRT. METHODS: Proton and carbon ion beam models in TRiP4D were converted from SyngoRT. Cubic plans with different depths in a water-tank phantom (WP) and previously treated and experimentally verified patient plans from SyngoRT were recalculated in TRiP4D. The target mean dose deviation (ΔDmean,T) and global gamma index (2%-2 mm for the absorbed dose and 3%-3mm for the RBE-weighted dose with 10% threshold) were evaluated. RESULTS: The carbon and proton absorbed dose gamma passing rates (γ-PRs) were ≥99.93% and ΔDmean,T smaller than -0.22%. On average, the RBE-weighted dose Dmean,T was -1.26% lower for TRiP4D than SyngoRT for cubic plans. In TRiP4D, the faster analytical 'low dose approximation' (Krämer, 2006) was used, while SyngoRT used a stochastic implementation (Krämer, 2000). The average ΔDmean, T could be reduced to -0.59% when applying the same biological effect calculation algorithm. However, the dose recalculation time increased by a factor of 79-477. ΔDmean,T variation up to -2.27% and -2.79% was observed for carbon absorbed and RBE-weighted doses in patient plans. The γ-PRs were ≥93.92% and ≥91.83% for patient plans, except for one proton beam with a range shifter (γ-PR of 64.19%). CONCLUSION: The absorbed dose between TRiP4D and SyngoRT were identical for both proton and carbon ion plans in the WP. Compared to SyngoRT, TRiP4D underestimated the target RBE-weighted dose; however more efficient in RBE-weighted dose calculation. Large variation for proton beam with range shifter was observed. TRiP4D will be used to evaluate doses delivered to moving targets. Uncertainties inherent to the 4D-dose reconstruction calculation are expected to be significantly larger than the dose errors reported here. For this reason, the residual differences between TRiP4D and SyngoRT observed in this study are considered acceptable. The study was approved by the Institutional Research Board of Shanghai Proton and Heavy Ion Center (approval number SPHIC-MP-2020-04, RS).

Emerging technologies for cancer therapy using accelerated particles.

Cancer therapy with accelerated charged particles is one of the most valuable biomedical applications of nuclear physics. The technology has vastly evolved in the past 50 years, the number of clinical centers is exponentially growing, and recent clinical results support the physics and radiobiology rationale that particles should be less toxic and more effective than conventional X-rays for many cancer patients. Charged particles are also the most mature technology for clinical translation of ultra-high dose rate (FLASH) radiotherapy. However, the fraction of patients treated with accelerated particles is still very small and the therapy is only applied to a few solid cancer indications. The growth of particle therapy strongly depends on technological innovations aiming to make the therapy cheaper, more conformal and faster. The most promising solutions to reach these goals are superconductive magnets to build compact accelerators; gantryless beam delivery; online image-guidance and adaptive therapy with the support of machine learning algorithms; and high-intensity accelerators coupled to online imaging. Large international collaborations are needed to hasten the clinical translation of the research results.

A likelihood-based particle imaging filter using prior information.

BACKGROUND: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. PURPOSE: This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. METHODS: The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. RESULTS: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. CONCLUSIONS: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Experimental Validation of a Real-Time Adaptive 4D-Optimized Particle Radiotherapy Approach to Treat Irregularly Moving Tumors.

PURPOSE: Treatment of locally advanced lung cancer is limited by toxicity and insufficient local control. Particle therapy could enable more conformal treatment than intensity modulated photon therapy but is challenged by irregular tumor motion, associated range changes, and tumor deformations. We propose a new strategy for robust, online adaptive particle therapy, synergizing 4-dimensional optimization with real-time adaptive beam tracking. The strategy was tested and the required motion monitoring precision was determined. METHODS AND MATERIALS: In multiphase 4-dimensional dose delivery (MP4D), a dedicated quasistatic treatment plan is delivered to each motion phase of periodic 4-dimensional computed tomography (4DCT). In the new extension, "MP4D with residual tracking" (MP4DRT), lateral beam tracking compensates for the displacement of the tumor center-of-mass relative to the current phase in the planning 4DCT. We implemented this method in the dose delivery system of a clinical carbon facility and tested it experimentally for a lung cancer plan based on a periodic subset of a virtual lung 4DCT (planned motion amplitude 20 mm). Treatments were delivered in a quality assurance-like setting to a moving ionization chamber array. We considered variable motion amplitudes and baseline drifts. The required motion monitoring precision was evaluated by adding noise to the motion signal. Log-file-based dose reconstructions were performed in silico on the entire 4DCT phantom data set capable of simulating nonperiodic motion. MP4DRT was compared with MP4D, rescanned beam tracking, and internal target volume plans. Treatment quality was assessed in terms of target coverage (D95), dose homogeneity (D5-D95), conformity number, and dose to heart and lung. RESULTS: For all considered motion scenarios and metrics, MP4DRT produced the most favorable metrics among the tested motion mitigation strategies and delivered high-quality treatments. The conformity was similar to static treatments. The motion monitoring precision required for D95 >95% was 1.9 mm. CONCLUSIONS: With clinically feasible motion monitoring, MP4DRT can deliver highly conformal dose distributions to irregularly moving targets.

Considerations for Upright Particle Therapy Patient Positioning and Associated Image Guidance.

Particle therapy is a rapidly growing field in cancer therapy. Worldwide, over 100 centers are in operation, and more are currently in construction phase. The interest in particle therapy is founded in the superior target dose conformity and healthy tissue sparing achievable through the particles' inverse depth dose profile. This physical advantage is, however, opposed by increased complexity and cost of particle therapy facilities. Particle therapy, especially with heavier ions, requires large and costly equipment to accelerate the particles to the desired treatment energy and steer the beam to the patient. A significant portion of the cost for a treatment facility is attributed to the gantry, used to enable different beam angles around the patient for optimal healthy tissue sparing. Instead of a gantry, a rotating chair positioning system paired with a fixed horizontal beam line presents a suitable cost-efficient alternative. Chair systems have been used already at the advent of particle therapy, but were soon dismissed due to increased setup uncertainty associated with the upright position stemming from the lack of dedicated image guidance systems. Recently, treatment chairs gained renewed interest due to the improvement in beam delivery, commercial availability of vertical patient CT imaging and improved image guidance systems to mitigate the problem of anatomical motion in seated treatments. In this review, economical and clinical reasons for an upright patient positioning system are discussed. Existing designs targeted for particle therapy are reviewed, and conclusions are drawn on the design and construction of chair systems and associated image guidance. Finally, the different aspects from literature are channeled into recommendations for potential upright treatment layouts, both for retrofitting and new facilities.

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

PURPOSE: Measurements comparing relative stopping power (RSP) accuracy of state-of-the-art systems representing single-energy and dual-energy computed tomography (SECT/DECT) with proton CT (pCT) and helium CT (HeCT) in biological tissue samples. METHODS: We used 16 porcine and bovine samples of various tissue types and water, covering an RSP range from 0.90 ± 0.06 to 1.78 ± 0.05. Samples were packed and sealed into 3D-printed cylinders ( d = 2  cm, h = 5  cm) and inserted into an in-house designed cylindrical polymethyl methacrylate (PMMA) phantom ( d = 10  cm, h = 10  cm). We scanned the phantom in a commercial SECT and DECT (120 kV; 100  and 140 kV/Sn (tin-filtered)); and acquired pCT and HeCT ( E ∼ 200  MeV/u, 2 ∘ steps, ∼ 6.2 × 10 6 (p)/ ∼ 2.3 × 10 6 (He) particles/projection) with a particle imaging prototype. RSP maps were calculated from SECT/DECT using stoichiometric methods and from pCT/HeCT using the DROP-TVS algorithm. We estimated the average RSP of each tissue per modality in cylindrical volumes of interest and compared it to ground truth RSP taken from peak-detection measurements. RESULTS: Throughout all samples, we observe the following root-mean-squared RSP prediction errors ± combined uncertainty from reference measurement and imaging: SECT 3.10 ± 2.88%, DECT 0.75 ± 2.80%, pCT 1.19 ± 2.81%, and HeCT 0.78 ± 2.81%. The largest mean errors ± combined uncertainty per modality are SECT 8.22 ± 2.79% in cortical bone, DECT 1.74 ± 2.00% in back fat, pCT 1.80 ± 4.27% in bone marrow, and HeCT 1.37 ± 4.25% in bone marrow. Ring artifacts were observed in both pCT and HeCT reconstructions, imposing a systematic shift to predicted RSPs. CONCLUSION: Comparing state-of-the-art SECT/DECT technology and a pCT/HeCT prototype, DECT provided the most accurate RSP prediction, closely followed by particle imaging. The novel modalities pCT and HeCT have the potential to further improve on RSP accuracies with work focusing on the origin and correction of ring artifacts. Future work will study accuracy of proton treatment plans using RSP maps from investigated imaging modalities.

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.

BACKGROUND: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality. MATERIAL AND METHODS: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods. RESULTS: The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads. CONCLUSION: The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality.

Helium radiography with a digital tracking calorimeter-a Monte Carlo study for secondary track rejection.

Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient. Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks. In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions. By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.

A scintillator-based range telescope for particle therapy.

The commissioning and operation of a particle therapy centre requires an extensive set of detectors for measuring various parameters of the treatment beam. Among the key devices are detectors for beam range quality assurance. In this work, a novel range telescope based on a plastic scintillator and read out by a large-scale CMOS sensor is presented. The detector is made of a stack of 49 plastic scintillator sheets with a thickness of 2-3 mm and an active area of 100 × 100 mm2, resulting in a total physical stack thickness of 124.2 mm. This compact design avoids optical artefacts that are common in other scintillation detectors. The range of a proton beam is reconstructed using a novel Bragg curve model that incorporates scintillator quenching effects. Measurements to characterise the performance of the detector were carried out at the Heidelberger Ionenstrahl-Therapiezentrum (HIT, Heidelberg, GER) and the Clatterbridge Cancer Centre (CCC, Bebington, UK). The maximum difference between the measured range and the reference range was found to be 0.41 mm at a proton beam range of 310 mm and was dominated by detector alignment uncertainties. With the new detector prototype, the water-equivalent thickness of PMMA degrader blocks has been reconstructed within ± 0.1 mm. An evaluation of the radiation hardness proves that the range reconstruction algorithm is robust following the deposition of 6,300 Gy peak dose into the detector. Furthermore, small variations in the beam spot size and transverse beam position are shown to have a negligible effect on the range reconstruction accuracy. The potential for range measurements of ion beams is also investigated.

Image quality of list-mode proton imaging without front trackers.

List mode proton imaging relies on accurate reconstruction of the proton most likely path (MLP) through the patient. This typically requires two sets of position sensitive detector systems, one upstream (front) and one downstream (rear) of the patient. However, for a clinical implementation it can be preferable to omit the front trackers (single-sided proton imaging). For such a system, the MLP can be computed from information available through the beam delivery system and the remaining rear tracker set. In this work, we use Monte Carlo simulations to compare a conventional double-sided (using both front and rear detector systems) with a single-sided system (only rear detector system) by evaluating the spatial resolution of proton radiographs (pRad) and proton CT images (pCT) acquired with these set-ups. Both the pencil beam spot size, as well as the spacing between spots was also adjusted to identify the impact of these beam parameters on the image quality. Relying only on the pencil beam central position for computing the MLP resulted in severe image artifacts both in pRad and pCT. Using the recently extended-MLP formalism that incorporate pencil beam uncertainty removed these image artifacts. However, using a more focused pencil beam with this algorithm induced image artifacts when the spot spacing was the same as the beam spot size. The spatial resolution tested with a sharp edge gradient technique was reduced by 40% for single-sided (MTF 10% = 3.0 lp/cm) compared to double-sided (MTF 10% = 4.9 lp/cm) pRad with ideal tracking detectors. Using realistic trackers the difference decreased to 30%, with MTF 10% of 4.0 lp/cm for the realistic double-sided and 2.7 lp/cm for the realistic single-sided setup. When studying an anthropomorphic paediatric head phantom both single- and double-sided set-ups performed similarly where the difference in water equivalent thickness (WET) between the two set-ups were less than 0.01 mm in homogeneous areas of the head. Larger discrepancies between the two set-ups were visible in high density gradients like the facial structures. A complete CT reconstruction of a Catphan[Formula: see text] module was performed. Assuming ideal detectors, the obtained spatial resolution was 5.1 lp/cm for double-sided and 3.8 lp/cm for the single-sided setup. Double- and single-sided pRad with realistic tracker properties returned a spatial resolution of 3.8 lp/cm and 3.2 lp/cm, respectively. Future studies should investigate the development of dedicated reconstruction algorithms targeted for single-sided particle imaging.

Theoretical considerations on the spatial resolution limit of single-event particle radiography.

Particle radiography (pRad) has been proposed and investigated as a promising tool for particle therapy as it provides a water equivalent thickness (WET) image of the patient. In single-event particle imaging, for each measured particle, the related most likely path (MLP) through the object is estimated to account for multiple Coulomb scattering (MCS). In previous studies, the accuracy limit of the MLP has been used to determine the spatial resolution limit. In this work, we investigate the limit of the spatial resolution achievable with different pRad algorithms based on a theoretical model of the particle scattering for an ideal beam and detector. First, we investigate binning the particles in a plane seated at the depth of the object of interest (plane-of-interest binning; PIB) and extend existing theoretical considerations also to objects not located in the binning plane. We use this to model the spatial resolution in case of binning the particles directly at the front or rear tracker (FTB and RTB, respectively). Further, we investigate evenly distributing the particles' WET along their trajectory into pixel channels and creating the pRad image as channel mean (along-path-binning; APB). Monte Carlo simulations are used to qualitatively investigate the different algorithms and to validate the theoretical predictions. We show that projecting the scattered particle paths onto a single image will inevitably result in a limited spatial resolution lower than expected from only the MLP uncertainty. Only in the case where the depth of a feature is known and used as binning depth for PIB, the spatial resolution of that feature is equal to the path estimation accuracy. For the APB algorithm the spatial resolution decreases with increasing depth in the object, especially if the true particle path through the object would be known. The derived theoretical models will be useful for future development of improved pRad reconstruction algorithms.

Improving single-event proton CT by removing nuclear interaction events within the energy/range detector.

Data filtering is crucial for accurate relative stopping power (RSP) reconstruction in proton CT (pCT). In this work, we assess different filters and their performance for the US pCT collaboration prototype pCT system in Monte Carlo (MC) simulations. The potential of using the recently proposed [Formula: see text]-E filter for removing nuclear interactions that occurred in the energy/range detector of the pCT system is investigated. Full pCT scans were acquired with the TOPAS MC simulated version of the prototype scanner that comprises two tracking detectors and a five stage energy/range detector. An ideal water cylinder and a water cylinder with five tissue inserts were investigated. Before image reconstruction, a [Formula: see text] WEPL filter was applied as the only filter, or in addition to filters acting on the energy deposit in each of the energy detector stages, as done currently with the prototype. The potential of the [Formula: see text]-E filter that was recently proposed for helium imaging was assessed. The results were compared to simulations for which nuclear interactions were disabled representing ground truth. The [Formula: see text] WEPL filter alone was not sufficient to filter out all nuclear interaction events and systematic fluctuations in the form of ring artifacts were present in the pCT reconstructed images. Applying energy filters currently used with the device prior to the [Formula: see text] WEPL filter only slightly improved the image quality. A [Formula: see text] WEPL filter improved the mean RSP accuracy, but could not fully remove the systematic fluctuations. The [Formula: see text]-E filter in addition to the current reconstruction procedure efficiently removed the systematic fluctuations and the achieved RSP accuracy closely matched the simulation without nuclear interactions. This study demonstrates the dependence of the accuracy of the usual [Formula: see text] WEPL filter on uncertainties arising within the energy detector. By enabling to remove such uncertainties, the [Formula: see text]-E method proved to yield some potential for improving the accuracy of pCT.

Design optimization of a pixel-based range telescope for proton computed tomography.

PURPOSE: A pixel-based range telescope for tracking particles during proton imaging is described. The detector applies a 3D matrix of stacked Monolithic Active Pixel Sensors with fast readout speeds. This study evaluates different design alternatives of the range telescope on basis of the protons' range accuracy and the track reconstruction efficiency. METHOD: Detector designs with different thicknesses of the energy-absorbing plates between each sensor layer are simulated using the GATE/Geant4 Monte Carlo software. Proton tracks traversing the detector layers are individually reconstructed, and a Bragg curve fitting procedure is applied for the calculation of each proton's range. RESULTS: Simulations show that the setups with 4 mm and thinner absorber layers of aluminum have a low range uncertainty compared to the physical range straggling, systematic errors below 0.3 mm water equivalent thickness and a track reconstruction capability exceeding ten million protons per second. CONCLUSIONS: In order to restrict the total number of layers and to yield the required tracking and range resolution properties, a design recommendation is reached where the proposed range telescope applies 3.5 mm thick aluminum absorber slabs between each sensor layer.

Experimental comparison of proton CT and dual energy x-ray CT for relative stopping power estimation in proton therapy.

Proton computed tomography (pCT) has been proposed as an alternative to x-ray computed tomography (CT) for acquiring relative to water stopping power (RSP) maps used for proton treatment planning dose calculations. In parallel, it has been shown that dual energy x-ray CT (DECT) improves RSP accuracy when compared to conventional single energy x-ray CT. This study aimed at directly comparing the RSP accuracy of both modalities using phantoms scanned at an advanced prototype pCT scanner and a state-of-the-art DECT scanner. Two phantoms containing 13 tissue-mimicking inserts of known RSP were scanned at the pCT phase II prototype and a latest generation dual-source DECT scanner (Siemens SOMATOM Definition FORCE). RSP accuracy was compared by mean absolute percent error (MAPE) over all inserts. A highly realistic Monte Carlo (MC) simulation was used to gain insight on pCT image artifacts which degraded MAPE. MAPE was 0.55% for pCT and 0.67% for DECT. The realistic MC simulation agreed well with pCT measurements ([Formula: see text]). Both simulation and experimental results showed ring artifacts in pCT images which degraded the MAPE compared to an ideal pCT simulation ([Formula: see text]). Using the realistic simulation, we could identify sources of artifacts, which are attributed to the interfaces in the five-stage plastic scintillator energy detector and calibration curve interpolation regions. Secondary artifacts stemming from the proton tracker geometry were also identified. The pCT prototype scanner outperformed a state-of-the-art DECT scanner in terms of RSP accuracy (MAPE) for plastic tissue mimicking inserts. Since artifacts tended to concentrate in the inserts, their mitigation may lead to further improvements in the reported pCT accuracy.

The impact of secondary fragments on the image quality of helium ion imaging.

Single-event ion imaging enables the direct reconstruction of the relative stopping power (RSP) information required for ion-beam therapy. Helium ions were recently hypothesized to be the optimal species for such technique. The purpose of this work is to investigate the effect of secondary fragments on the image quality of helium CT (HeCT) and to assess the performance of a prototype proton CT (pCT) scanner when operated with helium beams in Monte Carlo simulations and experiment. Experiments were conducted installing the U.S. pCT consortium prototype scanner at the Heidelberg Ion-Beam Therapy Center (HIT). Simulations were performed with the scanner using the TOPAS toolkit. HeCT images were reconstructed for a cylindrical water phantom, the CTP404 (sensitometry), and the CTP528 (line-pair) [Formula: see text] ® modules. To identify and remove individual events caused by fragmentation, the multistage energy detector of the scanner was adapted to function as a [Formula: see text] telescope. The use of the developed filter eliminated the otherwise arising ring artifacts in the HeCT reconstructed images. For the HeCT reconstructed images of a water phantom, the maximum RSP error was improved by almost a factor 8 with respect to unfiltered images in the simulation and a factor 10 in the experiment. Similarly, for the CTP404 module, the mean RSP accuracy improved by a factor 6 in both the simulation and the experiment when the filter was applied (mean relative error 0.40% in simulation, 0.45% in experiment). In the evaluation of the spatial resolution through the CTP528 module, the main effect of the filter was noise reduction. For both simulated and experimental images the spatial resolution was ∼4 lp cm-1. In conclusion, the novel filter developed for secondary fragments proved to be effective in improving the visual quality and RSP accuracy of the reconstructed images. With the filter, the pCT scanner is capable of accurate HeCT imaging.