Improve Dose Calculation Accuracy Research Articles

On the impact of improved dose calculation accuracy in clinical treatment planning for superficial high-dose-rate brachytherapy of extensive scalp lesions

TG-43-based dose calculations disregard tissue heterogeneities and finite scatter conditions, prompting the introduction of model-based dose calculation algorithms (MBDCAs) to improve accuracy in high-dose-rate (HDR) brachytherapy. This study evaluated the effectiveness of MBDCAs over TG-43 in HDR 192Ir brachytherapy of extended scalp lesions. Treatment planning dose calculations were compared with Monte Carlo (MC) data. TG-43 exhibited a dose overestimation ranging from 10% to 23% as the distance from the implant increased, while a better agreement from 2% to 6% was observed between the MBDCA and MC, supporting the adoption of MBDCAs for dose calculations in broad scalp lesions.

Heavy metal in radiation therapy: A simple method to differentiate hip prosthesis materials.

In recent years, the number of hip replacement patients receiving radiation therapy has steadily increased. In parallel, strategies have been developed to reduce metal artifacts in computed tomography (CT) images and improve the accuracy of dose calculation algorithms. However, in certain situations, knowledge of the type of prosthesis material is required to accurately determine the dose distribution. This study aims to identify physical materials in hip prostheses to correctly assign them in the treatment planning system and improve dose calculation accuracy. We first verified the validity of the extended CT mass density calibration curve measured on titanium (Ti) and stainless steel (SS) metal inserts of two different diameters. Then using dedicated reference objects of various circular diameters, we developed a method based on interpolation functions to differentiate between Ti and SS material groups. Forty data sets from 18 patients were used to validate our method on two different reconstruction kernels: a standard Br44f and the electron DirectDensity (Sd40f) kernels from Siemens. Hounsfield units (HU) of Ti and SS inserts were found to vary widely depending on insert diameter, CT spectrum, and reconstruction kernels due to cupping artifacts. The largest HU difference (-79%) was obtained for SS at 70kV with Br44f when the diameter increased from 8 to 30mm. Therefore, under these conditions, the extended CT-density calibration curve is not recommended for heavy metal density determination. Using our interpolation-based method, we achieved excellent detection (100%) and material differentiation (100%) results for stems in both reconstruction kernels. At CT energies between 110 and 140kV, the detection and material differentiation rates were 93.3% and 92.9% for the heads and 93.3% and 92.9% for the acetabular cups, respectively, with the Br44f. Similarly, the use of Sd40f resulted in detection and differentiation rates of 94.7% and 100% for the heads and 100% and 95.0% for the acetabular cups, respectively. This method makes it possible to differentiate between hip prosthesis materials and correctly assign them to the Ti or SS group without prior knowledge of the prosthesis type, regardless of the reconstruction kernels. In combination with the Acuros XB (Varian) or Monte Carlo dose algorithms, excellent dosimetric accuracy can be achieved even in the vicinity of hip prostheses. By performing basic measurements, the method can be adapted to other CT units and reconstruction kernels, replacing the use of an extended CT-density calibration curve.

Development of a proton CT imaging system using scintillator-based range detection.

The accuracy of proton therapy and preclinical proton irradiation experiments is susceptible to proton range uncertainties, which partly stem from the inaccurate conversion between CT numbers and relative stopping power (RSP). Proton computed tomography (PCT) can reduce these uncertainties by directly acquiring RSP maps. This study aims to develop a novel PCT imaging system based on scintillator-based proton range detection for accurate RSP reconstruction. The proposed PCT system consists of a pencil-beam brass collimator with a 1mm aperture, an object stage capable of translation and 360° rotation, a plastic scintillator for dose-to-light conversion, and a complementary metal oxide semiconductor (CMOS) camera for light distribution acquisition. A calibration procedure based on Monte Carlo (MC) simulation was implemented to convert the obtained light ranges into water equivalent ranges. The water equivalent path lengths (WEPLs) of the imaged object were determined by calculating the differences in proton ranges obtained with and without the object in the beam path. To validate the WEPL calculation, measurements of WEPLs for eight tissue-equivalent inserts were conducted. PCT imaging was performed on a custom-designed phantom and a mouse, utilizing both 60 and 360 projections. The filtered back projection (FBP) algorithm was employed to reconstruct the RSP from WEPLs. Image quality was assessed based on the reconstructed RSP maps and compared to reference and simulation-based reconstructions. The differences between the calibrated and reference ranges of 110-150 MeV proton beams were within 0.18mm. The WEPLs of eight tissue-equivalent inserts were measured with accuracies better than 1%. Phantom experiments exhibited good agreement with reference and simulation-based reconstructions, demonstrating average RSP errors of 1.26%, 1.38%, and 0.38% for images reconstructed with 60 projections, 60 projections after penalized weighted least-squares algorithm denoising, and 360 projections, respectively. Mouse experiments provided clear observations of mouse contours and major tissue types. MC simulation estimated an imaging dose of 3.44 cGy for decent RSP reconstruction. The proposed PCT imaging system enables RSP map acquisition with high accuracy and has the potential to improve dose calculation accuracy in proton therapy and preclinical proton irradiation experiments.

Estimation of the lateral variation of photon beam energy spectra using the percentage depth dose reconstruction method

In photon-collapsed cone convolution (pCCC) algorithm of the Monaco treatment planning system (TPS), the central-axis energy spectrum is assumed constant throughout the entire irradiation area. To consider lateral variations, an off-axis softening factor is applied to attenuation coefficients during the total energy released per unit mass calculation. We evaluated this method through comparison studies of percentage depth doses (PDDs) and off-axis ratios (OARs) calculated by Monaco and measured for a 6 MV photon beam at various off-axis angles and depths. Significant differences were observed, with relative differences exceeding ± 1%. Therefore, this method may not accurately represent lateral variations of energy spectra. We propose directly implementing energy spectra on both central-axis and off-axis to improve dose calculation accuracy for large field. To this end, we introduce reconstruction of PDDs from monoenergetic depth doses (MDDs) along off-axis angles, thereby estimating energy spectra as functions of radial distance. This method derives energy spectra quickly without significantly increasing the beam modeling time. MDDs were computed through Monte Carlo simulations (DOSRZnrc). The variances between reconstructed and measured PDDs were minimized using the generalized-reduced-gradient method to optimize energy spectra. Reconstructed PDDs along off-axis angles of 0°, 1.15°, 2.29°, 3.43°, 4.57°, 5.71°, 6.84°, 7.97°, 9.09°, 10.2° to estimate energy spectra at radial distances of 0–18 cm in 2 cm increments and OARs calculated using estimated energy spectra at 5, 10, and 20 cm depths, well agreed with measurement (relative differences within ± 0.5%). In conclusion, our proposed method accurately estimates lateral energy spectrum variation, thereby improving dose calculation accuracy of pCCC algorithm.

Study of Effective Atomic Number, Mass Energy Absorption Coefficient and Absorbed Dose Rate in Human Organ and Tissue Substitutes

Abstract: The effective atomic number and mass energy absorption coefficient are two essential parameters used to investigate the radiation response of composite materials of dosimetric interest in many medical applications. The effective atomic number of nine selected human tissues and eleven samples of tissue substitute materials were computed using two methods, the interpolation method and the direct method in the energy range used in brachytherapy applications (0.1-2.0) MeV. The applicability of using the effective atomic number values to investigate scatter and absorption properties of some tissue substitutes, against that of the corresponding human tissues, to validate their tissue-equivalency, is examined. The effect of partial interaction cross sections is also explicitly discussed. Further, the absorbed dose rate, for an isotropic point source, in the selected tissue samples was computed using their estimated mass energy absorption coefficient values. The obtained data are analyzed and differences in sample dose rate relative to water, for photon energies of interest, are evaluated. The results indicate that numbers of tissue substitute samples yield estimates of dose rate to within 5% of dose rate of their corresponding human tissues. The obtained results are expected to be useful in improving dose calculation accuracy in brachytherapy treatment planning or dose evaluation after treatment. Keywords: Dosimetry, Human organs and tissues, Tissue equivalent materials, Equivalent atomic number, Attenuation and absorption coefficients.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Improving dose calculation accuracy in preclinical radiation experiments using multi-energy element resolved cone-beam CT

Objective. Cone-beam CT (CBCT) in modern pre-clinical small-animal radiation research platforms provides volumetric images for image guidance and experiment planning purposes. In this work, we implemented multi-energy element-resolved (MEER) CBCT using three scans with different kVps on a SmART platform (Precision x-ray Inc.) to determine images of relative electron density (rED) and elemental composition (EC) that are needed for Monte Carlo-based radiation dose calculation. Approach. We performed comprehensive calibration tasks to achieve sufficient accuracy for this quantitative imaging purpose. For geometry calibration, we scanned a ball bearing phantom and used an analytical method together with an optimization approach to derive gantry angle specific geometry parameters. Intensity calibration and correction included the corrections for detector lag, glare, and beam hardening. The corrected CBCT projection images acquired at 30, 40, and 60 kVp in multiple scans were used to reconstruct CBCT images using the Feldkamp–Davis–Kress reconstruction algorithm. After that, an optimization problem was solved to determine images of rED and EC. We demonstrated the effectiveness of our CBCT calibration steps by showing improvements in image quality and successful material decomposition in cases with a small animal CT calibration phantom and a plastinated mouse phantom. Main results. It was found that artifacts induced by geometry inaccuracy, detector lag, glare, and beam hardening were visually reduced. CT number mean errors were reduced from 19% to 5%. In the CT calibration phantom case, median errors in H, O, and Ca fractions for all the inserts were below 1%, 2%, and 4% respectively, and median error in rED was less than 5%. Compared to the standard approach deriving material type and rED via CT number conversion, our approach improved Monte Carlo simulation-based dose calculation accuracy in bone regions. Mean dose error was reduced from 47.5% to 10.9%. Significance. The MEER-CBCT implemented on an existing CBCT system of a small animal irradiation platform achieved accurate material decomposition and significantly improved Monte Carlo dose calculation accuracy.

Evaluation of candidate template beam models for a matched TrueBeam treatment delivery system.

PurposeTo explore candidate RayStation beam models to serve as a class‐specific template for a TrueBeam treatment delivery system.MethodsEstablished validation techniques were used to evaluate three photon beam models: a clinically optimized model from the authors’ institution, the built‐in RayStation template, and a hybrid consisting of the RayStation template except substituting average MLC parameter values from a recent IROC survey. Comparisons were made for output factors, dose profiles from open fields, as well as representative VMAT test plans.ResultsFor jaw‐defined output factors, each beam model was within 1.6% of expected published values. Similarly, the majority (57–66%) of jaw‐defined dose curves from each model had a gamma pass rate >95% (2% / 3 mm, 20% threshold) when compared to TrueBeam representative beam data. For dose curves from MPPG 5.a MLC‐defined fields, average gamma pass rates (1% / 1 mm, 20% threshold) were 92.9%, 85.1%, and 86.0% for the clinical, template, and hybrid models, respectively. For VMAT test plans measured with a diode array detector, median dose differences were 0.6%, 1.3%, and 1.1% for the clinical, template, and hybrid models, respectively. For in‐phantom ionization chamber measurements with the same VMAT test plans, the average percent difference was −0.3%, −1.4%, and −1.0% for the clinical, template, and hybrid models, respectively.ConclusionBeam model templates taken from the vendor and aggregate results within the community were both reasonable starting points, but neither approach was as optimal as a clinically tuned model, the latter producing better agreement with all validation measurements. Given these results, the clinically optimized model represents a better candidate as a consensus template. This can benefit the community by reducing commissioning time and improving dose calculation accuracy for matched TrueBeam treatment delivery systems.

Metal artifact reduction method based on a constrained beam-hardening estimator for polychromatic x-ray CT

Beam hardening in x-ray computed tomography (CT) is inevitable because of the polychromatic x-ray spectrum and energy-dependent attenuation coefficients of materials, leading to the underestimation of artifacts arising from projection data, especially on metal regions. State-of-the-art research on beam-hardening artifacts is based on a numerical method that recursively performs CT reconstruction, which leads to a heavy computational burden. To address this computational issue, we propose a constrained beam-hardening estimator that provides an efficient numerical solution via a linear combination of two images reconstructed only once during the entire process. The proposed estimator reflects the geometry of metal objects and physical characteristics of beam hardening during the transmission of polychromatic x-rays through a material. Most of the associated parameters are numerically obtained from an initial uncorrected CT image and forward projection transformation without additional optimization procedures. Only the unknown parameter related to beam-hardening artifacts is fine-tuned by linear optimization, which is performed only in the reconstruction image domain. The proposed approach was systematically assessed using numerical simulations and phantom data for qualitative and quantitative comparisons. Compared with existing sinogram inpainting-based and model-based approaches, the proposed scheme in conjunction with the constrained beam-hardening estimator not only provided improved image quality in areas surrounding the metal but also achieved fast beam-hardening correction owing to the analytical reconstruction structure. This work may have significant implications in improving dose calculation accuracy or target volume delineation for treatment planning in radiotherapy.

Improving proton dose calculation accuracy by using deep learning

Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. The proposed model uses the PB dose and computed tomography image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1 mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 s. The trained model can be adapted to new datasets through transfer learning. Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

The dosimetric impact of stabilizing spinal implants in radiotherapy treatment planning with protons and photons: standard titanium alloy vs. radiolucent carbon-fiber-reinforced PEEK systems.

BackgroundThroughout the last years, carbon‐fibre‐reinforced PEEK (CFP) pedicle screw systems were introduced to replace standard titanium alloy (Ti) implants for spinal instrumentation, promising improved radiotherapy (RT) treatment planning accuracy. We compared the dosimetric impact of both implants for intensity modulated proton (IMPT) and volumetric arc photon therapy (VMAT), with the focus on uncertainties in Hounsfield unit assignment of titanium alloy.MethodsRetrospective planning was performed on CT data of five patients with Ti and five with CFP implants. Carbon‐fibre‐reinforced PEEK systems comprised radiolucent pedicle screws with thin titanium‐coated regions and titanium tulips. For each patient, one IMPT and one VMAT plan were generated with a nominal relative stopping power (SP) (IMPT) and electron density (ρ) (VMAT) and recalculated onto the identical CT with increased and decreased SP or ρ by ±6% for the titanium components.ResultsRecalculated VMAT dose distributions hardly deviated from the nominal plans for both screw types. IMPT plans resulted in more heterogeneous target coverage, measured by the standard deviation σ inside the target, which increased on average by 7.6 ± 2.3% (Ti) vs 3.4 ± 1.2% (CFP). Larger SPs lead to lower target minimum doses, lower SPs to higher dose maxima, with a more pronounced effect for Ti screws.ConclusionsWhile VMAT plans showed no relevant difference in dosimetric quality between both screw types, IMPT plans demonstrated the benefit of CFP screws through a smaller dosimetric impact of CT‐value uncertainties compared to Ti. Reducing metal components in implants will therefore improve dose calculation accuracy and lower the risk for tumor underdosage.

Improvement of metallic artifacts in computed tomography in the absence of artifact reduction algorithms for spinal treatment planning applications.

The goal of this research was to investigate if application of optimized imaging parameters, recommended in literature, would be effective in producing the image quality required for treatment planning of spinal radiation fields with metallic implants. CT images from an anthropomorphic torso phantom with and without spinal implants were acquired using different imaging protocols: raising kVp and mAs, reducing the pitch and applying an extended CT scale (ECTS) technique. Profiles of CT number (CT#) were produced using DICOM data of each image. The effect of artifact on dose calculation accuracy was investigated using the image data in the absence of implant as a reference and the recommended electron density tolerance levels (Δρe). Raising the kVp was the only method that produced improvement to some degree in CT# in artifact regions. Application of ECTS improved CT# values only for metal. Although raising the kVp was effective in reducing metallic artifact, the significance of this effect on Δρe values in corrected images depends on the required tolerance for treatment planning dose calculation accuracy. ECTS method was only successful in correcting the CT number range in the metal. Although, application of ECTS method did not have any effect on artifact regions, its use is necessary in order to improve delineation of metal and accuracy of attenuation calculations in metal, provided that the treatment planning system can use an extended CT# calibration curve. Also, for Monte Carlo calculations using patient's images, ECTS-post-processed-CT images improve dose calculation accuracy for impure metals.

Performance of a hybrid Monte Carlo-Pencil Beam dose algorithm for proton therapy inverse planning.

Analytical algorithms have a limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best trade-off between speed and accuracy for proton therapy plans. The hybrid algorithm calculates the optimal spot weights (w) by means of an iterative optimization process where the dose at each iteration is computed by using a precomputed dose influence matrix based on the conventional PB plus a correction term c obtained from a MC simulation. Updates of c can be triggered as often as necessary by calling the MC dose engine with the last corrected values of w as input. In order to analyze the performance of the hybrid algorithm against dose calculation errors, it was applied to a simplistic water phantom for which several test cases with different errors were simulated, including proton range uncertainties. Afterwards, the algorithm was used in three clinical cases (prostate, lung, and brain) and benchmarked against full MC-based optimization. The influence of different stopping criteria in the final results was also investigated. The hybrid algorithm achieved excellent results provided that the estimated range in a homogeneous material is the same for the two dose engines involved, i.e., PB and MC. For the three patient cases, the hybrid plans were clinically equivalent to those obtained with full MC-based optimization. Only a single update of c was needed in the hybrid algorithm to fulfill the clinical dose constraints, which represents an extra computation time to obtain c that ranged from 1 (brain) to 4min (lung) with respect to the conventional PB-based optimization, and an estimated average gain factor of 14 with respect to full MC-based optimization. The hybrid algorithm provides an improved trade-off between accuracy and speed. This algorithm can be immediately considered as an option for improving dose calculation accuracy of commercial analytical treatment planning systems, without a significant increase in the computation time (≪5min) with respect to current PB-based optimization.

A simplified Monte Carlo algorithm considering large-angle scattering for fast and accurate calculation of proton dose.

PurposeThe purpose of this study is to improve dose calculation accuracy of the simplified Monte Carlo (SMC) algorithm in the low‐dose region. Because conventional SMC algorithms calculate particle scattering in consideration of multiple Coulomb scattering (MCS) only, they approximate lateral dose profiles by a single Gaussian function. However, it is well known that the low‐dose region spreads away from the beam axis, and it has been pointed out that modeling of the low‐dose region is important to calculated dose accurately.MethodsA SMC algorithm, which is named modified SMC and considers not only MCS but also large angle scattering resembling hadron elastic scattering, was developed. In the modified SMC algorithm, the particle fluence varies in the longitudinal direction because the large‐angle scattering decreases residual range of particles in accordance with their scattering angle and tracking of the particles with large scattering angle is terminated at a short distance downstream from the scattering. Therefore, modified integrated depth dose (m‐IDD) tables, which are converted from measured IDD in consideration of the fluence loss, are used to calculate dose.ResultsIn the case of a 1‐liter cubic target, the calculation accuracy was improved in comparison with that of a conventional algorithm, and the modified algorithm results agreed well with Geant4‐based simulation results; namely, 98.8% of the points satisfied the 2% dose/2 mm distance‐to‐agreement (DTA) criterion. The calculation time of the modified SMC algorithm was 1972 s in the case of 4.4 × 108 particles and 16‐threading operation of an Intel Xeon E5‐2643 (3.3‐GHz clock).ConclusionsAn SMC algorithm that can reproduce a laterally widespread low‐dose region was developed. According to the comparison with a Geant4‐based simulation, it was concluded that the modified SMC algorithm is useful for calculating dose of proton radiotherapy.

Approaches to reducing photon dose calculation errors near metal implants.

Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging. Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare's o-mar, GE Healthcare's monochromatic gemstone spectral imaging (gsi) using dual-energy CT, and gsi with metal artifact reduction software (mars) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies. Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact reduction methods investigated, the authors found that o-mar was the most consistent method, resulting in either improved dose calculation accuracy (dental case) or little impact on calculation accuracy (spine case). gsi was unsuccessful at reducing the severe artifacts caused by dental fillings and had very little impact on calculation accuracy. gsi with mars on the other hand gave mixed results, sometimes introducing metal distortion and increasing calculation errors (titanium rectangular implant and titanium spinal hardware) but other times very successfully reducing artifacts (Cerrobend rectangular implant and dental fillings). Though successful at improving dose calculation accuracy upstream of metal implants, metal kernels were not found to substantially improve accuracy for clinical cases. Of the commercial artifact reduction methods investigated, o-mar was found to be the most consistent candidate for all-purpose CT simulation imaging. The mars algorithm for gsi should be used with caution for titanium implants, larger implants, and implants located near heterogeneities as it can distort the size and shape of implants and increase calculation errors.

SU‐F‐T‐441: Dose Calculation Accuracy in CT Images Reconstructed with Artifact Reduction Algorithm

Purpose:Accuracy of radiotherapy dose calculation in patients with surgical implants is complicated by two factors. First is the accuracy of CT number, second is the dose calculation accuracy. We compared measured dose with dose calculated on CT images reconstructed with FBP and an artifact reduction algorithm (OMAR, Philips) for a phantom with high density inserts. Dose calculation were done with Varian AAA and AcurosXB.Methods:A phantom was constructed with solid water in which 2 titanium or stainless steel rods could be inserted. The phantom was scanned with the Philips Brillance Big Bore CT. Image reconstruction was done with FBP and OMAR. Two 6 MV single field photon plans were constructed for each phantom. Radiochromic films were placed at different locations to measure the dose deposited. One plan has normal incidence on the titanium/steel rods. In the second plan, the beam is at almost glancing incidence on the metal rods. Measurements were then compared with dose calculated with AAA and AcurosXB.Results:The use of OMAR images slightly improved the dose calculation accuracy. The agreement between measured and calculated dose was best with AXB and image reconstructed with OMAR. Dose calculated on titanium phantom has better agreement with measurement. Large discrepancies were seen at points directly above and below the high density inserts. Both AAA and AXB underestimated the dose directly above the metal surface, while overestimated the dose below the metal surface. Doses measured downstream of metal were all within 3% of calculated values.Conclusion:When doing treatment planning for patients with metal implants, care must be taken to acquire correct CT images to improve dose calculation accuracy. Moreover, great discrepancies in measured and calculated dose were observed at metal/tissue interface. Care must be taken in estimating the dose in critical structures that come into contact with metals.

SU-F-T-600: Influence of Acuros XB and AAA Dose Calculation Algorithms On Plan Quality Metrics and Normal Lung Doses in Lung SBRT

Purpose: To study the influence of superposition-beam model (AAA) and determinant-photon transport-solver (Acuros XB) dose calculation algorithms on the treatment plan quality metrics and on normal lung dose in Lung SBRT. Methods: Treatment plans of 10 Lung SBRT patients were randomly selected. Patients were prescribed to a total dose of 50-54Gy in 3–5 fractions (10?5 or 18?3). Doses were optimized accomplished with 6-MV using 2-arcs (VMAT). Doses were calculated using AAA algorithm with heterogeneity correction. For each plan, plan quality metrics in the categories- coverage, homogeneity, conformity and gradient were quantified. Repeat dosimetry for these AAA treatment plans was performed using AXB algorithm with heterogeneity correction for same beam and MU parameters. Plan quality metrics were again evaluated and compared with AAA plan metrics. For normal lung dose, V20 and V5 to (Total lung- GTV) were evaluated. Results: The results are summarized in Supplemental Table 1. PTV volume was mean 11.4 (±3.3) cm3. Comparing RTOG 0813 protocol criteria for conformality, AXB plans yielded on average, similar PITV ratio (individual PITV ratio differences varied from −9 to +15%), reduced target coverage (−1.6%) and increased R50% (+2.6%). Comparing normal lung doses, the lung V20 (+3.1%) and V5 (+1.5%) were slightly higher for AXB plans compared to AAA plans. High-dose spillage ((V105%PD - PTV)/ PTV) was slightly lower for AXB plans but the % low dose spillage (D2cm) was similar between the two calculation algorithms. Conclusion: AAA algorithm overestimates lung target dose. Routinely adapting to AXB for dose calculations in Lung SBRT planning may improve dose calculation accuracy, as AXB based calculations have been shown to be closer to Monte Carlo based dose predictions in accuracy and with relatively faster computational time. For clinical practice, revisiting dose-fractionation in Lung SBRT to correct for dose overestimates attributable to algorithm may very well be warranted.

SU-G-BRC-17: Using Generalized Mean for Equivalent Square Estimation

Purpose: Equivalent Square (ES) is a widely used concept in radiotherapy. It enables us to determine many important quantities for a rectangular treatment field, without measurement, based on the corresponding values from its ES field. In this study, we propose a Generalized Mean (GM) type ES formula and compare it with other established formulae using benchmark datasets. Methods: Our GM approach is expressed as ES=(w•fx^α+(1-w)•fy^α)^(1/α), where fx, fy, α, and w represent field sizes, power index, and a weighting factor, respectively. When α=−1 it reduces to well-known Sterling type ES formulae. In our study, α and w are determined through least-square-fitting. Akaike Information Criterion (AIC) was used to benchmark the performance of each formula. BJR (Supplement 17) ES field table for X-ray PDDs and open field output factor tables in Varian TrueBeam representative dataset were used for validation. Results: Switching from α=−1 to α=−1.25, a 20% reduction in standard deviation of residual error in ES estimation was achieved for the BJR dataset. The maximum relative residual error was reduced from ∼3% (in Sterling formula) or ∼2% (in Vadash/Bjarngard formula) down to ∼1% in GM formula for open fields of all energies and at rectangular field sizes from 3cm to 40cm in the Varian dataset. The improvement of the GM over the Sterling type ES formulae is particularly noticeable for very elongated rectangular fields with short width. AIC analysis confirmed the superior performance of the GM formula after taking into account the expanded parameter space. Conclusion: The GM significantly outperforms Sterling type formulae at slightly increased computational cost. The GM calculation may nullify the requirement of data measurement for many rectangular fields and hence shorten the Linac commissioning process. Improved dose calculation accuracy is also expected by adopting the GM formula into treatment planning and secondary MU check systems.

TH-CD-202-04: Evaluation of Virtual Non-Contrast Images From a Novel Split-Filter Dual-Energy CT Technique

Purpose: To compare the ability of two dual-energy CT techniques, a novel split-filter single-source technique of superior temporal resolution against an established sequential-scan technique, to remove iodine contrast from images with minimal impact on CT number accuracy. Methods: A phantom containing 8 tissue substitute materials and vials of varying iodine concentrations (1.7–20.1 mg I /mL) was imaged using a Siemens Edge CT scanner. Dual-energy virtual non-contrast (VNC) images were generated using the novel split-filter technique, in which a 120kVp spectrum is filtered by tin and gold to create high- and low-energy spectra with < 1 second temporal separation between the acquisition of low- and high-energy data. Additionally, VNC images were generated with the sequential-scan technique (80 and 140kVp) for comparison. CT number accuracy was evaluated for all materials at 15, 25, and 35mGy CTDIvol. Results: The spectral separation was greater for the sequential-scan technique than the split-filter technique with dual-energy ratios of 2.18 and 1.26, respectively. Both techniques successfully removed iodine contrast, resulting in mean CT numbers within 60HU of 0HU (split-filter) and 40HU of 0HU (sequential-scan) for all iodine concentrations. Additionally, for iodine vials of varying diameter (2–20 mm) with the same concentration (9.9 mg I /mL), the system accurately detected iodine for all sizes investigated. Both dual-energy techniques resulted in reduced CT numbers for bone materials (by >400HU for the densest bone). Increasing the imaging dose did not improve the CT number accuracy for bone in VNC images. Conclusion: VNC images from the split-filter technique successfully removed iodine contrast. These results demonstrate a potential for improving dose calculation accuracy and reducing patient imaging dose, while achieving superior temporal resolution in comparison sequential scans. For both techniques, inaccuracies in CT numbers for bone materials necessitate consideration for radiation therapy treatment planning.

Simplified material assignment for cone beam computed tomography-based dose calculations of prostate radiotherapy with hip prostheses

AbstractObjectiveCone beam computed tomography (CBCT) images contain more scatter than a conventional computed tomography (CT) image and therefore provide inaccurate Hounsfield units (HUs). Consequently, CBCT images cannot be used directly for dose calculation. The aim of this study is to enable dose calculations to be performed with the use of CBCT images taken during radiotherapy and potentially avoid the necessity of re-planning.MethodologyA phantom and prostate cancer patient with a metallic prosthetic hip replacement were imaged using both CT and CBCT. The multilevel threshold algorithm was used to categorise pixel values in the CBCT images into segments of homogeneous HU. The variation in HU with position in the CBCT images was taken into consideration and the benefit of using a larger number of materials than typically used in previous work has been explored. This segmentation method relies upon the operator dividing the CBCT data into a set of volumes where the variation in the relationship between pixel values and HUs is small. A field-in-field treatment plan was generated from the CT of the phantom. An intensity-modulated radiation therapy plan was generated from CT images of the patient. These plans were then copied to the segmented CBCT datasets with identical settings and the doses were recalculated and compared.ResultsIn the phantom study,γevaluation showed that the percentage of points falling in planning target volume, rectum and bladder withγ&lt;1 (3%/3 mm) was 100%. In the patient study, increasing the number of bins to define the material type from seven materials to eight materials required 50% more operator time to improve the accuracy by 0·01% using pencil beam and collapsed cone and 0·05% when using Monte Carlo algorithms.ConclusionThe segmentation of CBCT images using the method in this study can be used for dose calculation. For a simple phantom, 2 values of HU were needed to improve dose calculation accuracy. In challenging circumstances such as that of a prostate patient with hip prosthesis, 5 values of HU were found to be needed, giving a reasonable balance between dose accuracy and operator time.