Stopping Power Ratio Research Articles

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Assessment of photon-counting Computed Tomography for quantitative imaging in radiation therapy: PCCT for quantitative imaging in radiotherapy.

To test a first generation clinical PCCT scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes. A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear, Norfolk, USA) was scanned on a NAEOTOM Alpha photon-counting CT (PCCT) and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers, Forscheim, Germany). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LAC) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via 'Rho/Z'-algorithm was also utilized. Proton stopping-power ratios (SPR) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations (RMSD) within the material regions were evaluated for LAC, RED/EAN and SPR, respectively. Two example ray-projections were also examined for LAC, SPR and water-equivalent thickness (WET), for illustrations of a more treatment-like scenario. There was a tendency towards narrower distributions for PCCT compared to both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored RMSDs showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile whereas the more standard projection showed similar results between the modalities. This study shows promising results for tissue characterization in a human-like geometry for radiotherapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Development of a Tumor Growth-responsive Robust Planning Method for Head and Neck Cancer in Carbon-ion Radiotherapy.

The current study aimed to evaluate a treatment planning method that is robust against tumor growth and to assess its effectiveness in particle therapy for head and neck cancer. The proposed method optimizes dose distribution by replacing the relative stopping power ratio (rSPR) of the clinical target volume (CTV) cavity region with a tumor-equivalent rSPR (Condition 1). The optimized initial treatment plan template was then recalculated using in-room CT images acquired in the same treatment position, and the doses to the tumor and organs at risk were compared with those in the initial treatment plan. We evaluated this method in 10 patients with head and neck cancer treated with carbon ion radiotherapy. To evaluate the effectiveness of the proposed method, we compared it to the initial treatment plan without the replacement (Condition 2). CTV V95% reduction relative to that of the initial treatment plan at the end of treatment was 1.3%±2.9% and 2.6%±3.7% for Condition 1 and Condition 2, respectively, with Condition 1 (Replacement condition) providing better CTV coverage. Subgroup analysis showed a higher change in target coverage in the mucosal melanoma than in the adenoid cystic carcinoma, suggesting that this was influenced by the rate of tumor growth. The proposed treatment planning method, which adjusts for tumor growth by modifying the rSPR in the cavity region, improved robustness in carbon ion radiotherapy for head and neck cancer. Condition 1 (with replacement) achieved better CTV coverage than Condition 2 (without replacement), particularly in fast-growing tumors like mucosal melanoma. This method ensures consistent dose delivery to tumors while maintaining safe doses to organs at risk, offering potential for improved treatment outcomes.

Theoretical investigation of dosimeter accuracy for linear energy transfer measurements in proton therapy: A comparative study of stopping power ratios

BackgroundAccurate measurement of linear energy transfer (LET) is crucial in medical physics, particularly for proton therapy dosimetry. High atomic-number (Zeff) materials such as BaFBr and low-Zeff materials such as Al2O3 and water are commonly used in dosimeters. PurposeTo evaluate the feasibility and accuracy of the use of various dosimetry materials (water, air, Al2O3, aluminum (Al), BaFBr, and oxygen) for measuring LET by comparing their stopping power (ratios) via the Bethe-Bloch theory and semiempirical models. MethodsStopping power ratios were calculated via the PSTAR database for proton energies ranging from 0.01 MeV to 10,000 MeV. The Bethe-Bloch theory with density and shell corrections was used for high-energy protons, whereas a semiempirical model was applied for low-energy protons. Calculations validation involved comparing the computed stopping powers SRIM-2008 and PSTAR for materials such as water, aluminum, air, Al2O3, BaFBr, and oxygen. ResultsThe stopping power water-to-air ratio remains stable, while the Al2O3-to-water and air-to-water ratios highlight their differing attenuation properties. The BaFBr-to-water ratio shows significant material-dependent differences, and the water-to-Al2O3 ratio is particularly relevant for proton therapy dosimetry calculations in medical physics. These results demonstrate consistency across materials but do not inherently confirm the accuracy of LET measurements. However, a comparison of theoretical models with computed stopping powers SRIM-2008 and PSTAR showed strong agreement, particularly for high-energy protons where the Bethe-Bloch theory was applied, suggesting that the models reliably predict stopping power at these energy levels. ConclusionsThis study confirms the feasibility of using high-Zeff materials such as BaFBr and low-Zeff materials such as Al2O3 and water for the use of LET measurements in proton therapy. The results validate the use of the Bethe-Bloch theory, computed stopping powers and semiempirical models in dosimetric applications, enhancing the precision of LET measurements and contributing to improved radiation therapy outcomes.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples

Objective. Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range in-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient’s tissue is often needed. Approach. A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample’s carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues. Results. The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous. Significance. Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute to in-vivo dose verification measurements.

Dosimetric impact of stopping power for human bone porosity with dual-energy computed tomography in scanned carbon-ion therapy treatment planning

Few reports have documented how the accuracy of stopping power ratio (SPR) prediction for porous bone tissue affects the dose distribution of scanned carbon-ion beam therapy. The estimated SPR based on single-energy computed tomography (SECT) and dual-energy CT (DECT) were compared for the femur of a Rando phantom which simulates the porosity of human bone, NEOBONE which is the hydroxyapatite synthetic bone substitute, and soft tissue samples. Dose differences between SECT and DECT were evaluated for a scanned carbon-ion therapy treatment plan for the Rando phantom. The difference in the water equivalent length was measured to extract the SPR of the examined samples. The differences for SPR estimated from the DECT-SPR conversion were small with − 1.8% and − 3.3% for the Rando phantom femur and NEOBONE, respectively, whereas the differences for SECT-SPR were between 7.6 and 70.7%, illustrating a 1.5-mm shift of the range and a dose difference of 13.3% at maximum point in the evaluation of the dose distribution. This study demonstrated that the DECT-SPR conversion method better estimated the SPR of the porosity of bone tissues than SECT-SPR followed by the accurate range of the carbon-ion beams on carbon-ion dose calculations.

Benefit of range uncertainty reduction in robust optimisation for proton therapy of brain, head-and-neck and breast cancer patients

Background and PurposeThe primary cause of range uncertainty in proton therapy is inaccuracy in estimating the stopping-power ratio from computed tomography. This study examined the impact on dose-volume metrics by reducing range uncertainty in robust optimisation for a diverse patient cohort and determined the level of range uncertainty that resulted in a relevant reduction in doses to organs-at-risk (OARs). Materials and MethodsThe effect of reducing range uncertainty on OAR doses was evaluated by robustly optimising six proton plans with varying range uncertainty levels (ranging from 3.5% in the original plan to 1.0%), keeping setup uncertainty fixed. All plans used the initial clinical treatment plan’s beam directions and optimisation objectives and were optimised until a clinically acceptable plan was achieved across all setup and range scenarios. The effect of reduced range uncertainty on dose-volume metrics for OARs near the target was evaluated. This study included 30 brain cancer patients, as well as five head-and-neck and five breast cancer patients, investigating the relevance of reducing range uncertainty when different setup uncertainties were used. ResultsLowering range uncertainty slightly reduced the nominal dose to surrounding tissue. For body volume receiving 80% of the prescribed dose, reducing range uncertainty from 3.5% to 2.0% resulted in a median decrease of 4 cm3 for the brain, 17 cm3 for head-and-neck, and 27 cm3 for breast cancer patients. ConclusionsReducing range uncertainty in robust optimisation showed a reduction in dose to OARs. The clinical relevance depends on the affected organs and the clinical dose constraints.

Diamond energy-dispersive dosimeter for measuring linear energy deposition distributions in clinical carbon beam therapy

A thin diamond membrane energy-dispersive dosimeter 50 μm thick was fabricated for estimating clinical dose distribution. In contrast to conventional semiconductors, diamond wide-bandgap semiconductors are expected to have excellent radiation tolerance and biological compatibility based on the linear relationship of stopping power ratio for clinical carbon particles. Linear energy transfer (LET) spectra were obtained along with the Bragg curve for a clinical carbon beam generated at Gunma University Heavy Ion Medical Center. The relative biological effectiveness (RBE) distributions were estimated from the LET spectra for both the mono-energy and spread-out Bragg peak irradiation configurations. The diamond detector was also used as a solid ionization chamber to obtain the radiation-induced current (RIC) distribution, which represents the physical dose distribution. The clinical dose distribution was estimated from the RIC and RBE distributions as proof of concept for the clinical carbon beam therapy field with a single-chip diamond energy-dispersive dosimeter.

Dosimetric characteristics of 3D-printed and epoxy-based materials for particle therapy phantoms

Objective3D printing has seen use in many fields of imaging and radiation oncology, but applications in (anthropomorphic) phantoms, especially for particle therapy, are still lacking. The aim of this work was to characterize various available 3D printing methods and epoxy-based materials with the specific goal of identifying suitable tissue surrogates for dosimetry applications in particle therapy.Methods3D-printed and epoxy-based mixtures of varying ratios combining epoxy resin, bone meal, and polyethylene powder were scanned in a single-energy computed tomography (CT), a dual-energy CT, and a µCT scanner. Their CT-predicted attenuation was compared to measurements in a 148.2 MeV proton and 284.7 MeV/u carbon ion beam. The sample homogeneity was evaluated in the respective CT images and in the carbon beam, additionally via widening of the Bragg peak. To assess long-term stability attenuation, size and weight measurements were repeated after 6–12 months.ResultsFour 3D-printed materials, acrylonitrile butadiene styrene polylactic acid, fused deposition modeling printed nylon, and selective laser sintering printed nylon, and various ratios of epoxy-based mixtures were found to be suitable tissue surrogates. The materials’ predicted stopping power ratio matched the measured stopping power ratio within 3% for all investigated CT machines and protocols, except for µCT scans employing cone beam CT technology. The heterogeneity of the suitable surrogate samples was adequate, with a maximum Bragg peak width increase of 11.5 ± 2.5%. The repeat measurements showed no signs of degradation after 6–12 months.ConclusionWe identified surrogates for soft tissue and low- to medium-density bone among the investigated materials. This allows low-cost, adaptable phantoms to be built for quality assurance and end-to-end tests for particle therapy.

2805: Monte Carlo analysis of particle fluences in C-12 beams for stopping power ratio determination

2805: Monte Carlo analysis of particle fluences in C-12 beams for stopping power ratio determination

Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers.

Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCTimages. To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinicalapplications. The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testingpurposes. The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results ofCBCT. We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.

Estimation of Proton Stopping Power Ratio and Mean Excitation Energy Using Electron Density and Its Applications via Machine Learning Approach.

The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e). The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods. All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM. The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre

Background and PurposeThis work introduces the first assessment of CT calibration following the ESTRO’s consensus guidelines and validating the HLUT through the irradiation of biological material. MethodsTwo electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider’s and ESTRO’s methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored. ResultsThe results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider’s and ESTRO’s methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%. ConclusionThis is the first report on calibrating an HLUT following the ESTRO’s guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO’s guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

Commissioning and Validation of CT Number to SPR Calibration in Carbon Ion Therapy Facility

PurposeWe performed computed tomography (CT)—stopping power ratio (SPR) calibration in a carbon-ion therapy facility and evaluated SPR estimation accuracy. Materials and MethodsA polybinary tissue model method was used for the calibration of CT numbers and SPR. As a verification by dose calculation, we created a virtual phantom to which the CT-SPR calibration table was applied. Then, SPR was calculated from the change in the range of the treatment planning beam when changing to 19 different CT numbers, and the accuracy of the treatment planning system (TPS) calculation of SPR values from the CT-SPR calibration table was validated. As a verification by measurement, 5 materials (water, milk, olive oil, ethanol, 40% K2HPO4) were placed in a container, and the SPR was obtained by measurement from the change in the range of the beam that passed through the materials. ResultsThe results of the dose calculations of the TPS showed that the results agreed within 1% for the lower CT numbers up to 1000 HU, but there was a difference of 3.0% in the higher CT number volume. The difference between the SPR calculated by TPS and the SPR caused by the difference in the energy of the incident particles agreed within 0.51%. The accuracy of SPR estimation was measured, and the error was within 2% for all materials tested. ConclusionThese results indicate that the SPR estimation errors are within the range of errors that can be expected in particle therapy. From commissioning and verification results, the CT-SPR calibration table obtained during this commissioning process is clinically applicable.

Generation and evaluation of anatomy-preserving virtual CT for online adaptive proton therapy.

Daily CTs generated by CBCT correction are required for daily replanning in online-adaptive proton therapy (APT) to effectively deal with inter-fractional changes. Out of the currently available methods, the suitability of a daily CT generation method for proton dose calculation also depends on the anatomical site. We propose an anatomy-preserving virtual CT (APvCT) method as a hybrid method of CBCT correction, which is especially suitable for large anatomy deformations. The accuracy of the hybrid method was assessed by comparison with the corrected CBCT (cCBCT) and virtual CT (vCT) methods in the context of online APT. Seventy-one daily CBCTs of four prostate cancer patients treated with intensity modulated proton therapy (IMPT) were converted to daily CTs using cCBCT, vCT, and the newly proposed APvCT method. In APvCT, planning CT (pCT) were mapped to CBCT geometry using deformable image registration with boundary conditions on controlling regions of interest (ROIs) created with deep learning segmentation on cCBCT. The relative frequency distribution (RFD) of HU, mass density and stopping power ratio (SPR) values were assessed and compared with the pCT. The ROIs in the APvCT and vCT were compared with cCBCT in terms of Dice similarity coefficient (DSC) and mean distance-to-agreement (mDTA). For each patient, a robustly optimized IMPT plan was created on the pCT and subsequent daily adaptive plans on daily CTs. For dose distribution comparison on the same anatomy, the daily adaptive plans on cCBCT and vCT were recalculated on the corresponding APvCT. The dose distributions were compared in terms of isodose volumes and 3D global gamma-index passing rate (GPR) at γ(2%, 2mm) criterion. For all patients, no noticeable difference in RFDs was observed amongst APvCT, vCT, and pCT except in cCBCT, which showed a noticeable difference. The minimum DSC value was 0.96 and 0.39 for contours in APvCT and vCT respectively. The average value of mDTA for APvCT was 0.01cm for clinical target volume and ≤0.01cm for organs at risk, which increased to 0.18cm and ≤0.52cm for vCT. The mean GPR value was 90.9%, 64.5%, and 67.0% for APvCT versus cCBCT, vCT versus cCBCT, and APvCT versus vCT, respectively. When recalculated on APvCT, the adaptive cCBCT and vCT plans resulted in mean GPRs of 89.5±5.1% and 65.9±19.1%, respectively. The mean DSC values for 80.0%, 90.0%, 95.0%, 98.0%, and 100.0% isodose volumes were 0.97, 0.97, 0.97, 0.95, and 0.91 for recalculated cCBCT plans, and 0.89, 0.88, 0.87, 0.85, and 0.81 for recalculated vCT plans. Hausdorff distance for the 100.0% isodose volume in some cases of recalculated cCBCT plans on APvCT exceeded 1.00cm. APvCT contours showed good agreement with reference contours of cCBCT which indicates anatomy preservation in APvCT. A vCT with erroneous anatomy can result in an incorrect adaptive plan. Further, slightly lower values of GPR between the APvCT and cCBCT-based adaptive plans can be explained by the difference in the cCBCT's SPR RFD from the pCT.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition.

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction. To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy. A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups. The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR. The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

Investigation of optimal combination of monochromatic image of dual-energy CT system for proton range calculation

PurposeTo explore the optimal combination of monochromatic images derived from dual-energy CT (DECT) in calculating the stopping power ratio (SPR) of human tissues. MethodsMonochromatic CT numbers ranging from 40 keV to 140 keV (in intervals of 10 keV) for 34 standard human tissues were theoretically computed based on the NIST database. These values were then paired, resulting in 55 different dual-energy combinations for calculating the stopping power ratio (SPR). The combinations adhered to the rule of a lower energy (Elow) paired with a higher X-ray energy (Ehigh). Subsequently, each energy combination was utilized to predict the SPR of human tissues, and the accuracy of each pairing was assessed. Additionally, an analysis of the impact of uncertainty in the attenuation coefficient was conducted. ResultsWhen Elow is ≤ 70 keV, an optimal energy combination emerges. These optimal energy combinations within the 40 keV, 50 keV, 60 keV, and 70 keV groups are 40–100 keV, 50–90 keV, 60–80 keV, and 70–80 keV, respectively, with corresponding mean absolute errors (MAE) of SPR at 0.12%, 0.11%, 0.12%, and 0.15%, respectively. The impact of attenuation coefficient uncertainty on these findings is minimal; even with a 1–2% uncertainty, the maximum change in MAE for SPR error is only 0.04%. ConclusionsDual-energy CT utilizing energy pairs within the low energy range (40–70 keV) exhibits greater advantages in predicting the stopping power ratio of human tissues. In our investigation, the four energy combinations—40-100 keV, 50–90 keV, 60–80 keV, and 70-80 keV—demonstrated the highest accuracy in predicting SPR.

Effects of modern aesthetic dental fillings on proton therapy

Background and purposeHigh-density dental fillings pose a non-negligible impact on head and neck cancer treatment. For proton therapy, stopping power ratio (SPR) prediction will be significantly impaired by the associated image artifacts. Dose perturbation is also inevitable, compromising the treatment plan quality. While plenty of work has been done on metal or amalgam fillings, none has touched on composite resin (CR) and glass ionomer cement (GIC) which have seen an increasing usage. Hence, this work aims to provide a detailed characterisation of SPR and dose perturbation in proton therapy caused by CR and GIC. Materials and methodsFour types of fillings were used: CR, Fuji Bulk (FB), Fuji II (FII) and Fuji IX (FIX). The latter three belong to GIC category. Measured SPR were compared with SPR predicted using single-energy computed tomography (SECT) and dual-energy computed tomography (DECT). Dose perturbation of proton beams with lower- and higher-energy levels was also quantified using Gafchromic films. ResultsThe measured SPR for CR, FB, FII and FIX were 1.68, 1.77, 1.77 and 1.76, respectively. Overall, DECT could predict SPR better than SECT. The lowest percentage error achieved by DECT was 19.7 %, demonstrating the challenge in estimating SPR, even for fillings with relatively lower densities. For both proton beam energies and all four fillings of about 4.5 mm thickness, the maximum dose perturbation was 3 %. ConclusionThis study showed that dose perturbation by CR and GIC was comparatively small. We have measured and recommended the SPR values for overriding the fillings in TPS.

Proton Online Adaptation Using Novel Cone-Beam Computed Tomography System

Cone beam computed tomography (CBCT) has been used in clinic frequently to provide quick, online, and handy three-dimensional images. However, due to its significant artifacts and inaccurate Hounsfield values (HU), CBCT based online proton adaptation is infeasible. Recently, a novel in-room imaging solution was developed to allow larger images, better contrast and faster CBCT imaging acquisition. Our work demonstrated for the first time the feasibility of using this novel CBCT images for direct intensity modulated proton planning. Three patients and three CIRS phantoms were scanned using novel imaging technique (CBCTp) for this work. CT curves were acquired by scanning a CIRS electron density phantom. Stopping power ratio was computed using the stoichiometric method. Proton plans were made on treatment simulation CT and further evaluated on CBCTp to compare the differences. The table below shows three patients and three phantoms plan comparisons. Multiple sites and dose levels were studied. The planning target volume (PTV) coverage (D95%) and mean dose difference between simulation CT and CBCTp are 0.8% and 0.3%, correspondingly. Dosimetrically, phantom and patient plans are almost identical between two imaging techniques. Lung patient plan show the largest variation due to patient tumor change and the quality of breathing. The novel and high quality of CBCT is feasible for direct proton planning with accurate CT calibration. It provides a reliable alternative solution for proton online adaptive therapy.

A discrete ordinates Boltzmann solver for application to inverse planning of photons and protons

The aim of this work is to develop a discrete ordinates Boltzmann solver that can be used for calculation of absorbed dose from both photons and protons within an inverse planning optimiser, so as to perform accurate dose calculation throughout the whole of the inverse planning process. With photons, five transport sweeps were performed to obtain scattered photon fluence, and unscattered electron fluence was then obtained and used as a fixed source for solution of the electron transport equations. With protons, continuous slowing down was treated as a fixed source, and five transport sweeps were used to calculate scattered fluence. The total electron or proton fluence was multiplied by the stopping power ratio for the transport medium to obtain absorbed dose. The method was evaluated in homogeneous media and in a lung case where the planning target volume was surrounded by low-density lung material. Photon arc, proton passive scattering and proton arc treatments were considered. The results were compared to a clinically validated convolution dose calculation for photons, and with an analytical method for protons. In water-equivalent media, the discrete ordinates method agrees with the alternative algorithms to within 2%. Convergence is found to be sufficiently complete for water-, lung- and bone-equivalent materials after five iterations. The dose calculated by the relatively simple angular quadrature is seen to be very close to that calculated by a more comprehensive quadrature. For inhomogeneous lung plans, the method shows more heterogeneity of dose to the planning target volume than the comparative methods. The discrete ordinates Boltzmann solver provides a general framework for dose calculation with both photons and protons. The method is suitable for incorporation into an inverse planning optimiser, so that accurate dose calculation in a heterogeneous medium can be obtained throughout inverse planning, with the result that the final dose distribution is as predicted by the optimiser.