Stopping Power Ratio Research Articles

Estimating Proton Stopping Power Ratio of a Novel Iodinated Rectal Hydrogel Spacer Using Dual Energy Computed Tomography

A novel iodinated rectal hydrogel spacer (I-RS) has been developed to make the gel radiopaque on CT compared to the standard rectal hydrogel spacers (S-RS). As the iodine in I-RS strongly absorbs kV photons, the accuracy of currently employed CT calibration built into a single energy CT (SECT) becomes inaccurate to determine the proton stopping power ratio (PSR) of the injected gel. Direct measurements of PSR mediated by I-RS with proton beams has been reported; however, those measurements were conducted under ideal conditions without fully accounting for density heterogeneity mediated by air bubbles co-injected with the gel during placement. This study used dual-energy CT (DECT) to estimate I-RS PSR in patients undergoing CT simulation for prostate cancer (PCa) radiotherapy (RT).Sequential scans at 90 and 140 kVp were acquired for 7 intact PCa (I-RS n = 5, S-RS n = 2) on a Philips Brilliance scanner. PSR was reconstructed following the method proposed by Torikoshi and improved by Yang, and compared to similar measurements when I-RS was replaced with S-RS. A pre-calculated table correlating effective atomic number with all possible Hounsfield Unit (HU) combinations from two sequential scans was implemented to accelerate reconstruction. PSRs were compared between DECT and SECT, and between I-RS and S-RS.In gel, HU measurements were heterogeneous (range -372 - +358). The mean PSR on reconstruction when using DECT was 0.966 (0.940-0.980) for I-RS. Replacement of I-RS with S-RS HU density resulted in a mean PSR of 0.937 (0.926-0.941) by DECT. PSR for I-RS on SECT was 1.107, and 1.014 for S-RS. The estimated difference between PSR in I-RS and S-RS on DECT vs. SECT were 14% and 8%, respectively.These data demonstrate that the use of I-RS in proton RT treatment planning could overestimate proton range due to underestimating PSR when CT simulation is performed using standard SECT. Notably, PSR absolute value reported here is different compared to previously reported in vitro data under ideal conditions and may be related to differences in gel heterogeneity in vivo. Heterogeneity is highlighted by the wide range of gel HU measurements. Direct measurements of PSR will be obtained in a planned future experiment. These data suggest that PSR overestimation due to I-RS should be considered when planning PCa treatment using proton therapy for patients who get I-RS to reduce treatment toxicity.

Cema‐based formalism for the determination of absorbed dose for high‐energy photon beams

Determination of absorbed dose is well established in many dosimetry protocols and considered to be highly reliable using ionization chambers under reference conditions. If dosimetry is performed under other conditions or using other detectors, however, open questions still remain. Such questions frequently refer to appropriate correction factors. A converted energy per mass (cema)-based approach to formulate such correction factors offers a good understanding of the specific response of a detector for dosimetry under various measuring conditions and thus an estimate of pros and cons of its application. Determination of absorbed dose requires the knowledge of the beam quality correction factor kQ,Qo , where Q denotes the quality of a user beam and Qo is the quality of the radiation used for calibration. In modern Monte Carlo (MC)-based methods, kQ,Qo is directly derived from the MC-calculated dose conversion factor, which is the ratio between the absorbed dose at a point of interest in water and the mean absorbed dose in the sensitive volume of an ion chamber. In this work, absorbed dose is approximated by the fundamental quantity cema. This approximation allows the dose conversion factor to be substituted by the cema conversion factor. Subsequently, this factor is decomposed into a product of cema ratios. They are identified as the stopping power ratio water to the material in the sensitive detector volume, and as the correction factor for the fluence perturbation of the secondary charged particles in the detector cavity caused by the presence of the detector. This correction factor is further decomposed with respect to the perturbation caused by the detector cavity and that caused by external detector properties. The cema-based formalism was subsequently tested by MC calculations of the spectral fluence of the secondary charged particles (electrons and positrons) under various conditions. MC calculations demonstrate that considerable fluence perturbation may occur particularly under non-reference conditions. Cema-based correction factors to be applied in a 6-MV beam were obtained for a number of ionization chambers and for three solid-state detectors. Feasibility was shown at field sizes of 4×4 and 0.5cm ×0.5cm. Values of the cema ratios resulting from the decomposition of the dose conversion factor can be well correlated with detector response. Under the small field conditions, the internal fluence correction factor of ionization chambers is considerably dependent on volume averaging and thus on the shape and size of the cavity volume. The cema approach is particularly useful at non-reference conditions including when solid-state detectors are used. Perturbation correction factors can be expressed and evaluated by cema ratios in a comprehensive manner. The cema approach can serve to understand the specific response of a detector for dosimetry to be dependent on (a) radiation quality, (b) detector properties, and (c) electron fluence changes caused by the detector. This understanding may also help to decide which detector is best suited for a specific measurement situation.

Monte Carlo-derived ionization chamber correction factors in therapeutic carbon ion beams

The accuracy of electromagnetic transport in the GEANT4 Monte Carlo (MC) code was investigated for carbon ion beams and ionization chamber (IC)-specific beam quality correction factors were calculated. This work implemented a Fano cavity test for carbon ion beams in the 100–450 MeV/u energy range to assess the accuracy of the default electromagnetic physics parameters. The Urban and the Wentzel-VI multiple Coulomb scattering models were evaluated and the impact of maxStep, dRover, and final range parameters on the accuracy of the transport algorithm was investigated. The optimal production thresholds for an accurate calculation of values, which is the product of the water-to-air stopping power ratio and the IC-specific perturbation correction factor, were also studied. The correction factors were calculated for a cylindrical and a parallel-plate IC using carbon ions in the 150–450 MeV/u energy range. Modifying the default electromagnetic physics parameters resulted in a maximum deviation from theory of 0.3%. Therefore, the default EM parameters were used for the remainder of this work. The factors were found to converge for both ICs with decreasing production threshold distance below 5 μm. The values obtained in this work agreed with the TRS-398 stopping power ratios and other previously reported results within uncertainty. This study highlights an accurate MC-based technique to calculate the combined stopping power ratio and the perturbation correction factor for any IC in carbon ion beams.

KV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study.

This study aims to estimate the proton stopping power ratio (SPR) by using 80-120kV and 120kV-6MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations. Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnIm. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied. With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT. The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required.

Stopping-power ratios for electron beams used in total skin electron therapy.

The electron beams for total skin electron therapy (TSET) are often degraded by a scatter plate in addition to extended distances. For electron dosimetry, both the AAPM TG-51 and IAEA TRS-398 recommend the use of two formulas developed by Burns et al [Med. Phys. 23, 489-501 (1996)] to estimate the water-to-air stopping-power ratios (SPRs). Both formulas are based on a fit to SPRs calculated for standard electron beams. This study aims to find: (1) if the formulas are applicable to beams used in TSET and (2) the impact of the ICRU report 90 recommendations on the SPRs for these beams. The EGSnrc Monte Carlo code system is used to generate 6MeV high dose rate total skin electron (HDTSe) beams used in TSET. The simulated beams are used to calculate dose distributions and SPRs as a function of depth in a water phantom. The fitted SPRs using the empirical formulas are compared with MC-calculated SPRs. The electron beam quality specifier, the depth in water at which the absorbed dose falls to 50% of its maximum value, R50 , decreases approximately 1mm for each additional 100-cm extended distance ranging from 2.24cm at SSD=100 to 1.72cm at SSD=700cm. For beams passing through a scatter plate, R50 is 1.76cm (1.14) at SSD=300 and 1.48cm (0.85cm) at SSD=600cm with an Acrylic plate thickness of 3mm (9mm), respectively. The discrepancy between fitted and MC-calculated SPRs at dref as a function of R50 is <0.8%, and in many cases <0.4%. The difference between fitted and MC-calculated SPRs as a function of depth and R50 is within 1% at depths <0.8R50 for beams with R50 ≥1.14cm. The ICRU-90 recommendations decrease SPRs by 0.3%-0.4% compared to the use of data recommended in ICRU-37. The formulas used by the major protocols are accurate enough for clinical beams used in TSET and the error caused using the formulas is <1% to estimate SPRs as a function of depth and R50 for depths <0.8R50 for beams used in TSET with R50 ≥1.14cm. The impact of the ICRU-90 recommendations shows a decrease of SPRs by a fraction of a percent for beams used in TSET.

PO-1582 Spencer-Attix stopping power ratios for flattening filter and flattening filter free photon beams

PO-1582 Spencer-Attix stopping power ratios for flattening filter and flattening filter free photon beams

Ion chamber and film-based quality assurance of mixed electron-photon radiation therapy.

In previous work, we demonstrated that mixed electron-photon radiation therapy (MBRT) produces treatment plans with improved normal tissue sparing and similar target coverage, when compared to photon-only plans. The purpose of this work was to validate the MBRT delivery process on a Varian TrueBeam accelerator and laying the groundwork for a patient-specific quality assurance (QA) protocol based on ion chamber point measurements and 2D film measurements. MC beam models used to calculate the MBRT dose distributions of each modality (photons/electrons) were validated with a single-angle beam MBRT treatment plan delivered on a slab of Solid Water phantom with a film positioned at a depth of 2cm. The measured film absorbed dose was compared to the calculated dose. To validate clinical deliveries, a polymethyl methacrylate (PMMA) cylinder was machined and holes were made to fit an ionization chamber. A complex MBRT plan involving a photon arc and three electron delivery angles was created with the aim of reproducing a clinically realistic dose distribution in typical soft tissue sarcoma tumours of the extremities. The treatment plan was delivered on the PMMA cylinder. Point measurements were taken with an Exradin A1SL chamber at two nominal depths: 1.4cm and 2.1cm. The plan was also delivered on a second identical phantom with an insert at 2cm depth, where a film was placed. An existing EGSnrc user-code, SPRRZnrc, was modified to calculate the stopping power ratios between any materials in the same voxelized geometry used for dose calculation purposes. This modified code, called SPRXYZnrc, was used to calculate a correction factor, , accounting for the differences in electron fluence spectrum at the measurement point compared to that at reference conditions. The uncertainty associated with neglecting potential ionization chamber fluence perturbation correction factors using this approach was estimated. The film measurement from the Solid Water phantom treatment plan was in good agreement with the simulated dose distribution, with a gamma pass rate of 96.1% for a 3%/2mm criteria. For the PMMA phantom delivery, for the same gamma criteria, the pass rate was 97.3%. The ion chamber measurements of the total delivered dose agreed with the MC-simulated dose within 2.1%. The beam quality correction factors amounted to, at most, a 4% correction on the ion chamber measurement. However, individual contribution of low electron energies proved difficult to precisely measure due to their steep dose gradients, with disagreements of up to 28%±15% at 2.1cm depth (6MeV). Ion chamber measurement procedure of electron beams was achieved in less than 5min, and the entire validation process including phantom setup was performed in less than 30min. The agreement between measured and simulated MBRT doses indicates that the dose distributions obtained from the MBRT treatment planning algorithm are realistically achievable. The SPRXYZnrc MC code allowed for convenient calculations of simultaneously with the dose distributions, laying the groundwork for patient-specific QA protocol practical for clinical use. Further investigation is needed to establish the accuracy of our ionization chamber correction factors calculations at low electron energies.

Implementation and experimental evaluation of Mega-voltage fan-beam CT using a linear accelerator

BackgroundMega-voltage fan-beam Computed Tomography (MV-FBCT) holds potential in accurate determination of relative electron density (RED) and proton stopping power ratio (SPR) but is not widely available.ObjectiveTo demonstrate the feasibility of MV-FBCT using a medical linear accelerator (LINAC) with a 2.5 MV imaging beam, an electronic portal imaging device (EPID) and multileaf collimators (MLCs).MethodsMLCs were used to collimate MV beam along z direction to enable a 1 cm width fan-beam. Projection data were acquired within one gantry rotation and preprocessed with in-house developed artifact correction algorithms before the reconstruction. MV-FBCT data were acquired at two dose levels: 30 and 60 monitor units (MUs). A Catphan 604 phantom was used to evaluate basic image quality. A head-sized CIRS phantom with three configurations of tissue-mimicking inserts was scanned and MV-FBCT Hounsfield unit (HU) to RED calibration was established for each insert configuration using linear regression. The determination coefficient ({R}^{2}) was used to gauge the accuracy of HU-RED calibration. Results were compared with baseline single-energy kilo-voltage treatment planning CT (TP-CT) HU-RED calibration which represented the current standard clinical practice.ResultsThe in-house artifact correction algorithms effectively suppressed ring artifact, cupping artifact, and CT number bias in MV-FBCT. Compared to TP-CT, MV-FBCT was able to improve the prediction accuracy of the HU-RED calibration curve for all three configurations of insert materials, with {R}^{2} > 0.9994 and {R}^{2} < 0.9990 for MV-FBCT and TP-CT HU-RED calibration curves of soft-tissue inserts, respectively. The measured mean CT numbers of blood-iodine mixture inserts in TP-CT drastically deviated from the fitted values but not in MV-FBCT. Reducing the radiation level from 60 to 30 MU did not decrease the prediction accuracy of the MV-FBCT HU-RED calibration curve.ConclusionWe demonstrated the feasibility of MV-FBCT and its potential in providing more accurate RED estimation.

Proton stopping power prediction based on dual-energy CT-generated virtual monoenergetic images.

The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images. A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated. The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone. The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.

Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy

PurposeExperimental assessment of inter-centre variation and absolute accuracy of stopping-power-ratio (SPR) prediction within 17 particle therapy centres of the European Particle Therapy Network. Material and methodsA head and body phantom with seventeen tissue-equivalent materials were scanned consecutively at the participating centres using their individual clinical CT scan protocol and translated into SPR with their in-house CT-number-to-SPR conversion. Inter-centre variation and absolute accuracy in SPR prediction were quantified for three tissue groups: lung, soft tissues and bones. The integral effect on range prediction for typical clinical beams traversing different tissues was determined for representative beam paths for the treatment of primary brain tumours as well as lung and prostate cancer. ResultsAn inter-centre variation in SPR prediction (2σ) of 8.7%, 6.3% and 1.5% relative to water was determined for bone, lung and soft-tissue surrogates in the head setup, respectively. Slightly smaller variations were observed in the body phantom (6.2%, 3.1%, 1.3%). This translated into inter-centre variation of integral range prediction (2σ) of 2.9%, 2.6% and 1.3% for typical beam paths of prostate-, lung- and primary brain-tumour treatments, respectively. The absolute error in range exceeded 2% in every fourth participating centre. The consideration of beam hardening and the execution of an independent HLUT validation had a positive effect, on average. ConclusionThe large inter-centre variations in SPR and range prediction justify the currently clinically used margins accounting for range uncertainty, which are of the same magnitude as the inter-centre variation. This study underlines the necessity of higher standardisation in CT-number-to-SPR conversion.

Validation of polylactic acid polymer as soft tissue substitutive in radiotherapy

We report the experimental study on the physical and radiation properties of polylactic acid polymer (PLA-C3H4O2) combined with Monte Carlo simulations for application in radiotherapy. The suitability of PLA for phantoms construction as a substitute for soft human tissue in MeV photon radiation dosimetry and therapy is discussed. The elemental composition and radiation properties are described as recommended by ICRU 44. In addition, Hounsfield unit (HU), mass attenuation coefficient, mass stopping power and tissue phantom ratio were determined. Previous works on this subject presented mainly the properties of the raw material formula, neglecting above all the effects of the printing matrix and commercial raw materials used for PLA's production. The present upon irradiation properties of polylactic acid polymer were contrasted to those known of solid water and attested the viability and performance of PLA to bio-mimic soft tissue. The experimental and simulated data demonstrate the adequacy of PLA to printed phantoms provided that the printer infills result in a density of 1.05–1.09 g/cm3.

First-In-Human Validation of CT-Based Proton Range Prediction Using Prompt Gamma Imaging in Prostate Cancer Treatments

Uncertainty in computed tomography (CT)-based range prediction substantially impairs the accuracy of proton therapy. Direct determination of the stopping-power ratio (SPR) from dual-energy CT (DECT) has been proposed (DirectSPR), and initial validation studies in phantoms and biological tissues have proven a high accuracy. However, a thorough validation of range prediction in patients has not yet been achieved by any means. Here, we present the first systematic validation of CT-based proton range prediction in patients using prompt gamma imaging (PGI). A PGI slit camera system with improved positioning accuracy, using a floor-based docking station, was used. Its overall uncertainty for range prediction validation was determined experimentally with both x-ray and beam measurements. The accuracy of range prediction in patients was determined from clinical PGI measurements during hypofractionated treatment of 5 patients with prostate cancer - in total 30 fractions with in-room control-CTs. For each pencil-beam-scanning spot, the range shift was obtained by comparing the PGI measurement to a control-CT-based PGI simulation. Three different SPR prediction approaches were applied in simulations: a standard CT-number-to-SPR conversion (Hounsfield look-up table [HLUT]), an adapted HLUT (DECT optimized), andDirectSPR. The spot-wise weighted mean range shift from all spots served as a measure for the accuracy of the respective range prediction approach. A mean range prediction accuracy of 0.0% ± 0.5%, 0.3% ± 0.4%, and 1.8% ± 0.4% was obtained for DirectSPR, adapted HLUT, and standard HLUT, respectively. The overall validation uncertainty of the second-generation PGI slit camera is about 1mm (2σ) for all approaches, which is smaller than the range prediction uncertainty for deep-seated tumors. For the first time, range prediction accuracy was assessed in clinical routine using PGI range verification in prostate cancer treatments. Both DECT-derived range prediction approaches agree well with the measured proton range from PGI verification, whereas the standard HLUT approach differs relevantly. These results endorse the recent reduction of clinical safety margins in DirectSPR-based treatment planning in our institution.

Dual-Energy Computed Tomography Proton-Dose Calculation with Scripting and Modified Hounsfield Units.

PurposeTo describe an implementation of dual-energy computed tomography (DECT) for calculation of proton stopping-power ratios (SPRs) in a commercial treatment-planning system. The process for validation and the workflow for safe deployment of DECT is described, using single-energy computed tomography (SECT) as a safety check for DECT dose calculation.Materials and MethodsThe DECT images were acquired at 80 kVp and 140 kVp and were processed with computed tomography scanner software to derive the electron density and effective atomic number images. Reference SPRs of tissue-equivalent plugs from Gammex (Middleton, Wisconsin) and CIRS (Computerized Imaging Reference Systems, Norfolk, Virginia) electron density phantoms were used for validation and comparison of SECT versus DECT calculated through the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California) application programming interface scripting tool. An in-house software was also used to create DECT SPR computed tomography images for comparison with the script output. In the workflow, using the Eclipse system application programming interface script, clinical plans were optimized with the SECT image set and then forward-calculated with the DECT SPR for the final dose distribution. In a second workflow, the plans were optimized using DECT SPR with reduced range-uncertainty margins.ResultsFor the Gammex phantom, the root mean square error in SPR was 1.08% for DECT versus 2.29% for SECT for 10 tissue-surrogates, excluding the lung. For the CIRS Phantom, the corresponding results were 0.74% and 2.27%. When evaluating the head and neck plan, DECT optimization with 2% range-uncertainty margins achieved a small reduction in organ-at-risk doses compared with that of SECT plans with 3.5% range-uncertainty margins. For the liver case, DECT was used to identify and correct the lipiodol SPR in the SECT plan.ConclusionIt is feasible to use DECT for proton-dose calculation in a commercial treatment planning system in a safe manner. The range margins can be reduced to 2% in some sites, including the head and neck.

Monte Carlo study of dosimetric impact of gadolinium contrast medium in transverse field MR-Linac system.

The aim of this study is to evaluate the dosimetric impact of gadolinium contrast medium (Gadovist) in a transverse MR-Linac system using Monte Carlo methods. The dose distributions were calculated using two heterogeneous multi-layer phantoms consisting of Gadovist, water, bone, and lung. The photon beam was irradiated with a filed size of 5×5cm2, and a transverse magnetic field of 0-3.0T was applied perpendicular to the incident photon beam. Next, dose distributions for brain, head and neck (H&N), and lung cancer patients were calculated using a patient voxel-based phantom with and without replacing the patient's GTV with Gadovist. The dose at the water-Gadovist interface increased by 8% without a magnetic field. A similar dose increment was observed at 0.35T. In contrast, the dose increment at the water-Gadovist interface was small at 1.5T and a dose decrement of 5% was observed at 3.0T. The dose variation at the lung-Gadovist interface was larger than that at the water-Gadovist interface. The mass collision stopping power ratio for Gadovist was 7% lower than that for water, whereas, the electron fluence spectra at the water-Gadovist interface increased by 17.5%. In a patient study, Gadovist increased the Dmean for brain, H&N, and lung cancer patients by 0.65-8.9%. The dose variation due to Gadovist grew large in the low-dose region in H&N and lung cancer. The GTV dose variation due to Gadovist in all treatment site was below 2% at 0-3T if the Gadovist concentration was lower than 0.2mmol/ml-1.

Technical Note: validation of a material assignment method for a retrospective study of carbon-ion radiotherapy using Monte Carlo simulation.

We propose a two-step method to converse human tissue materials from patient computed tomography (CT) images, which is required in dose reconstructions for a retrospective study of carbon-ion radiotherapy (CIRT) using Monte Carlo (MC) simulation. The first step was to assign the standard tissues of the International Commission on Radiological Protection reference phantoms according to the CT-number. The second step was to determine the mass density of each material based on the relationship between CT-number and stopping power ratio (Hounsfield unit [HU]-SPR) registered in treatment planning system (TPS). Direct implementation of the well-calibrated HU-SPR curve allows the reproduction of previous clinical treatments recorded in TPS without uncertainty due to a mismatch of the CT scanner or scanning conditions, whereas MC simulation with realistic human tissue materials can fulfill the out-of-field dose, which was missing in the record. To validate our proposed method, depth-dose distributions in the homogenous and heterogeneous phantoms irradiated by a 400 MeV/u carbon beam with an 8 cm spread-out Bragg peak (SOBP) were computed by the MC simulation in combination with the proposed methods and compared with those of TPS. Good agreement of the depth-dose distributions between the TPS and MC simulation (within a 1% discrepancy in range) was obtained for different materials. In contrast, fluence distributions of secondary particles revealed the necessity of MC simulation using realistic human tissue. The proposed material assignment method will be used for a retrospective study using previous clinical data of CIRT at the National Institute of Radiological Sciences (NIRS).

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 (), the primary proton dose (), and the ProtonDSE corrected detector dose (). 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% for 2.2% ± 1.4% for and 2.5% ± 2.0% for

Changes of KRISS primary standards by implementing ICRU 90 recommendation

The International Commission of Radiation Units and Measurements (ICRU) published report No. 90 (i.e., Key Data for Ionizing Radiation Dosimetry: Measurement Standards and Applications) in 2016. The present study explores the impact of ICRU 90 on the primary standards of the Korea Research Institute of Standards and Science (KRISS). To this end, first, two new correction factors (kii and kw) for the free-air ionization chambers were introduced; it influences the standards related to kV X-ray beams. The new correction factors were calculated by simulating the initial electrons’ generations using the GEANT4 code. The second recommendation considered was for changes in the mean excitation energies of graphite and water, and the use of the crystalline density of graphite in the stopping power evaluation. These recommendations should be adopted for the graphite-walled cavity chamber and graphite calorimeter, which are the primary standards of the KRISS for air kerma in gamma rays and absorbed dose to water in high-energy photons, respectively. EGSnrc code-based evaluations of the electron stopping-power ratios for gamma rays were performed for the KRISS graphite-walled cavity chamber. Regarding graphite calorimetry in high-energy photon beams, the recommendations on graphite and water have been studied and presented in a previous paper. The third consideration is the recommendation for increasing uncertainty of the mean energy of electrons expended in dry air per ion pair production, $${\left(W/e\right)}_{\mathrm{air}}$$ , from 0.15% to 0.35%. The impact of the increased uncertainty under the conditions of the use of the existing components was summarized, together with the overall changes to the KRISS primary standards related to air kerma. Consequently, the difference between the products of the calculated kii and kW for the free-air ionization chamber standard and the corresponding values given in the ICRU 90 report was less than 0.10% for mono-energetic photons in the range of 1–400 keV. Regarding the X-ray beams recommended by the Consultative Committee for Ionizing Radiation (CCRI), the calculated correction factors deviated from those of the Bureau International des Poids et Mesures (BIPM) standards by 0.03%. In the case of the graphite-walled cavity chamber, changes of 0.65–0.77% were observed for the gamma-ray air kerma standards. The observed changes were reasonable, considering that similar changes were found in those reported by other research groups. Regarding the combined uncertainties, the uncertainties for the air kerma primary standards increased up to approximately 0.40%, primarily because of $${\left(W/e\right)}_{air}$$ . The changes to the KRISS primary standards that were observed in the present study will be adopted for the measurements performed since 2021.

Monte Carlo computation of 3D distributions of stopping power ratios in light ion beam therapy using GATE-RTion.

This paper presents a novel method for the calculation of three-dimensional (3D) Bragg-Gray water-to-detector stopping power ratio (sw,det ) distributions for proton and carbon ion beams. Contrary to previously published fluence-based calculations of the stopping power ratio, the sw,det calculation method used in this work is based on the specific way GATE/Geant4 scores the energy deposition. It only requires the use of the so-called DoseActor, as available in GATE, for the calculation of the sw,det at any point of a 3D dose distribution. The simulations are performed using GATE-RTion v1.0, a dedicated GATE release that was validated for the clinical use in light ion beam therapy. The Bragg-Gray water-to-air stopping power ratio (sw,air ) was calculated for monoenergetic proton and carbon ion beams with the default stopping power data in GATE-RTion v1.0 and the new ICRU90 recommendation. The sw,air differences between the use of the default and the ICRU90 configuration were 0.6% and 5.4% at the physical range (R80 - 80% dose level in the distal dose fall-off) for a 70MeV proton beam and a 120MeV/u carbon ion beam, respectively. For protons, the sw,det results for lithium fluoride, silicon, gadolinium oxysulfide, and the active layer material of EBT2 (radiochromic film) were compared with the literature and a reasonable agreement was found. For a real patient treatment plan, the 3D distributions of sw,det in proton beams were calculated. Our method was validated by comparison with available literature data. Its equivalence with Bragg-Gray cavity theory was demonstrated mathematically. The capability of GATE-RTion v1.0 for the sw,det calculation at any point of a 3D dose distribution for simple and complex proton and carbon ion plans was presented.

Animal tissue-based quantitative comparison of dual-energy CT to SPR conversion methods using high-resolution gel dosimetry

Dual-energy computed tomography (DECT) has been shown to allow for more accurate ion therapy treatment planning by improving the estimation of tissue stopping power ratio (SPR) relative to water, among other tissue properties. In this study, we measured and compared the accuracy of SPR values derived using both dual- and single-energy CT (SECT) based on different published conversion algorithms. For this purpose, a phantom setup containing either fresh animal soft tissue samples (beef, pork) and a water reference or tissue equivalent plastic materials was designed and irradiated in a clinical proton therapy facility. Dosimetric polymer gel was positioned downstream of the samples to obtain a three-dimensional proton range distribution with high spatial resolution. The mean proton range in gel for each tissue relative to the water sample was converted to a SPR value. Additionally, the homogeneous samples were probed with a variable water column encompassed by two ionization chambers to benchmark the SPR accuracy of the gel dosimetry. The SPR values measured with both methods were consistent with a mean deviation of 0.2%, but the gel dosimetry captured range variations up to 5 mm within individual samples.Across all fresh tissue samples the SECT approach yielded significantly greater mean absolute deviations from the SPR deduced using gel range measurements, with an average difference of 1.2%, compared to just 0.3% for the most accurate DECT-based algorithm. These results show a significant advantage of DECT over SECT for stopping power prediction in a realistic setting, and for the first time allow to compare a large set of methods under the same conditions.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.