Towards an objective evaluation of tolerances for beam modeling in a treatment planning system

Technical note: Point-by-point ion-recombination correction for accurate dose profile measurement in high dose-per-pulse irradiation field

BACKGROUND

Although flattening filter free (FFF) beams are commonly used in clinical treatment, the accuracy of dose measurements in FFF beams has been questioned. Higher dose per pulse (DPP) such as FFF beams from a linear accelerator may cause problems in dose profile measurements using an ionization chamber due to the change of the charge collection efficiency. Ionization chambers are commonly used for percent depth dose (PDD) measurements. Changes of DPP due to chamber movement during PDD measurement can vary the ion collection efficiency of ionization chambers. In the case of FF beams, the DPP fluctuation is negligible, but in the case of the FFF beams, the DPP is 2.5 ∼ 4 times larger than that of the FF beam, and the change in ion collection efficiency is larger than that of the FF beam. PDD profile normalized by maximum dose depth, 10 cm depth for example, may therefore be affected by the ion collection efficiency.

PURPOSE

In this study, we investigate the characteristics of the ion collection efficiency change depending on the DPP of each ionization chamber in the FFF beam. We furthermore propose a method to obtain the chamber- independent PDD by applying a DPP-dependent ion recombination correction.

METHODS

Prior to investigating the relationship between DPP and charge collection efficiency, Jaffe-plots were generated with different DPP settings to investigate the linearity between the applied voltage and collected charge. The absolute dose measurement using eight ionization chambers under the irradiation settings of 0.148, 0.087, and 0.008 cGy/pulse were performed. Applied voltages for the Jaffe-plots were 100, 125, 150, 200, 250, and 300 V. The ion recombination correction factor Pion was calculated by the two-voltage analysis (TVA) method at the applied voltages of 300 and 100 V. The DPP dependency of the charge collection efficiency for each ionization chamber were evaluated from the DPP- Pion plot. PDD profiles for the 10 MV FFF beam were measured using Farmer type chambers (TN30013, FC65-P, and FC65-G) and mini-type chambers (TN31010, TN31021, CC13, CC04, and FC23-C). The PDD profiles were corrected with ion recombination correction at negative and positive polar applied voltages of 100 and 300 V.

RESULTS

From the DPP-Pion relation for each ionization chamber with DPP ranging from 0.008 cGy/pulse to 0.148 cGy/pulse, all Farmer and mini-type chambers satisfied the requirements described in AAPM TG-51 addendum. However, Pion for the CC13 was most affected by DPP among tested chambers. The maximum deviation among PDDs using eight ionization chambers for 10 MV FFF was about 1%, but the deviation was suppressed to about 0.5% by applying ion recombination correction at each depth.

CONCLUSIONS

In this study, the deviation of PDD profile among the ionization chambers was reduced by the ion recombination coefficient including the DPP dependency, especially for high DPP beams such as FFF beams. The present method is particularly effective for CC13, where the ion collection efficiency is highly DPP dependent.

Report of AAPM Task Group 155: Megavoltage photon beam dosimetry in small fields and non-equilibrium conditions

Small-field dosimetry used in advance treatment technologies poses challenges due to loss of lateral charged particle equilibrium (LCPE), occlusion of the primary photon source, and the limited choice of suitable radiation detectors. These challenges greatly influence dosimetric accuracy. Many high-profile radiation incidents have demonstrated a poor understanding of appropriate methodology for small-field dosimetry. These incidents are a cause for concern because the use of small fields in various specialized radiation treatment techniques continues to grow rapidly. Reference and relative dosimetry in small and composite fields are the subject of the International Atomic Energy Agency (IAEA) dosimetry code of practice that has been published as TRS-483 and an AAPM summary publication (IAEA TRS 483; Dosimetry of small static fields used in external beam radiotherapy: An IAEA/AAPM International Code of Practice for reference and relative dose determination, Technical Report Series No. 483; Palmans et al., Med Phys 45(11):e1123, 2018). The charge of AAPM task group 155 (TG-155) is to summarize current knowledge on small-field dosimetry and to provide recommendations of best practices for relative dose determination in small megavoltage photon beams. An overview of the issue of LCPE and the changes in photon beam perturbations with decreasing field size is provided. Recommendations are included on appropriate detector systems and measurement methodologies. Existing published data on dosimetric parameters in small photon fields (e.g., percentage depth dose, tissue phantom ratio/tissue maximum ratio, off-axis ratios, and field output factors) together with the necessary perturbation corrections for various detectors are reviewed. A discussion on errors and an uncertainty analysis in measurements is provided. The design of beam models in treatment planning systems to simulate small fields necessitates special attention on the influence of the primary beam source and collimating devices in the computation of energy fluence and dose. The general requirements for fluence and dose calculation engines suitable for modeling dose in small fields are reviewed. Implementations in commercial treatment planning systems vary widely, and the aims of this report are to provide insight for the medical physicist and guidance to developers of beams models for radiotherapy treatment planning systems.

Evaluation of differences and dosimetric influences of beam models using golden and multi-institutional measured beam datasets in radiation treatment planning systems

PURPOSE

The beam model in radiation treatment planning systems (RTPSs) plays a crucial role in determining the accuracy of calculated dose distributions. The purpose of this study was to ascertain differences in beam models and their dosimetric influences when a golden beam dataset (GBD) and multi-institution measured beam datasets (MBDs) are used for beam modeling in RTPSs.

METHODS

The MBDs collected from 15 institutions, and the MBDs' beam models, were compared with a GBD, and the GBD's beam model, for Varian TrueBeam linear accelerator. The calculated dose distributions of the MBDs' beam models were compared with those of the GBD's beam model for simple geometries in a water phantom. Calculated dose distributions were similarly evaluated in volumetric modulated arc therapy (VMAT) plans for TG-119 C-shape and TG-244 head and neck, at several dose constraints of the planning target volumes (PTVs), and organs at risk.

RESULTS

The agreements of the MBDs with the GBD were almost all within ±1%. The calculated dose distributions for simple geometries in a water phantom also closely corresponded between the beam models of GBD and MBDs. Nevertheless, there were considerable differences between the beam models. The maximum differences between the mean energy of the energy spectra of GBD and MBDs were -0.12 MeV (-10.5%) in AcurosXB (AXB, Eclipse) and 0.11 MeV (7.7%) in collapsed cone convolution (CCC, RayStation). The differences in the VMAT calculated dose distributions varied for each dose region, plan, X-ray energy, and dose calculation algorithm. The ranges of the differences in the dose constraints were -5.6% to 3.0% for AXB and -24.1% to 2.8% for CCC. In several VMAT plans, the calculated dose distributions of GBD's beam model tended to be lower in high-dose regions and higher in low-dose regions than those of the MBDs' beam models.

CONCLUSIONS

We found that small differences in beam data have large impacts on the beam models, and on calculated dose distributions in clinical VMAT plan, even if beam data correspond within ±1%. GBD's beam model was not a representative beam model. The beam models of GBD and MBDs and their calculated dose distributions under clinical conditions were significantly different. These differences are most likely due to the extensive variation in the beam models, reflecting the characteristics of beam data. The energy spectrum and radial energy in the beam model varied in a wide range, even if the differences in the beam data were <±1%. To minimize the uncertainty of the calculated dose distributions in clinical plans, it was best to use the institutional MBD for beam modeling, or the beam model that ensures the accuracy of calculated dose distributions.

Reference dataset of users' photon beam modeling parameters for the Eclipse, Pinnacle, and RayStation treatment planning systems

PURPOSE

The aim of this work was to provide a novel description of how the radiotherapy community configures treatment planning system (TPS) radiation beam models for clinically used treatment machines. Here we describe the results of a survey of self-reported TPS beam modeling parameter values across different C-arm linear accelerators, beam energies, and multileaf collimator (MLC) configurations.

ACQUISITION AND VALIDATION METHODS

Beam modeling data were acquired via electronic survey implemented through the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center's online facility questionnaire. The survey was open to participation from January 2018 through January 2019 for all institutions monitored by IROC. After quality control, 2818 beam models were collected from 642 institutions. This survey, designed for Eclipse, Pinnacle, and RayStation, instructed physicists to report parameter values used to model the radiation source and MLC for each treatment machine and beam energy used clinically for intensity-modulated radiation therapy. Parameters collected included the effective source/spot size, MLC transmission, dosimetric leaf gap, tongue and groove effect, and other nondosimetric parameters specific to each TPS. To facilitate survey participation, instructions were provided on how to identify requested beam modeling parameters within each TPS environment.

DATA FORMAT AND USAGE NOTES

Numeric values of the beam modeling parameters are compiled and tabulated according to TPS and calculation algorithm, linear accelerator model class, beam energy, and MLC configuration. Values are also presented as distributions, ranging from the 2.5th to the 97.5th percentile.

POTENTIAL APPLICATIONS

These data provide an independent guide describing how the radiotherapy community mathematically represents its clinical radiation beams. These distributions may be used by the community for comparison during the commissioning or verification of their TPS beam models. Ultimately, we hope that the current work will allow institutions to spot potentially suspicious parameter values and help ensure more accurate radiotherapy delivery.

The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

On the sensitivity of TG-119 and IROC credentialing to TPS commissioning errors

We investigate the sensitivity of IMRT commissioning using the TG‐119 C‐shape phantom and credentialing with the IROC head and neck phantom to treatment planning system commissioning errors. We introduced errors into the various aspects of the commissioning process for a 6X photon energy modeled using the analytical anisotropic algorithm within a commercial treatment planning system. Errors were implemented into the various components of the dose calculation algorithm including primary photons, secondary photons, electron contamination, and MLC parameters. For each error we evaluated the probability that it could be committed unknowingly during the dose algorithm commissioning stage, and the probability of it being identified during the verification stage. The clinical impact of each commissioning error was evaluated using representative IMRT plans including low and intermediate risk prostate, head and neck, mesothelioma, and scalp; the sensitivity of the TG‐119 and IROC phantoms was evaluated by comparing dosimetric changes to the dose planes where film measurements occur and change in point doses where dosimeter measurements occur. No commissioning errors were found to have both a low probability of detection and high clinical severity. When errors do occur, the IROC credentialing and TG 119 commissioning criteria are generally effective at detecting them; however, for the IROC phantom, OAR point‐dose measurements are the most sensitive despite being currently excluded from IROC analysis. Point‐dose measurements with an absolute dose constraint were the most effective at detecting errors, while film analysis using a gamma comparison and the IROC film distance to agreement criteria were less effective at detecting the specific commissioning errors implemented here.

PACS number: 87.55.Qr

Utilizing knowledge from prior plans in the evaluation of quality assurance

Increased interest regarding sensitivity of pre-treatment intensity modulated radiotherapy and volumetric modulated arc radiotherapy (VMAT) quality assurance (QA) to delivery errors has led to the development of dose-volume histogram (DVH) based analysis. This paradigm shift necessitates a change in the acceptance criteria and action tolerance for QA. Here we present a knowledge based technique to objectively quantify degradations in DVH for prostate radiotherapy. Using machine learning, organ-at-risk (OAR) DVHs from a population of 198 prior patients' plans were adapted to a test patient's anatomy to establish patient-specific DVH ranges. This technique was applied to single arc prostate VMAT plans to evaluate various simulated delivery errors: systematic single leaf offsets, systematic leaf bank offsets, random normally distributed leaf fluctuations, systematic lag in gantry angle of the mutli-leaf collimators (MLCs), fluctuations in dose rate, and delivery of each VMAT arc with a constant rather than variable dose rate.Quantitative Analyses of Normal Tissue Effects in the Clinic suggests V75Gy dose limits of 15% for the rectum and 25% for the bladder, however the knowledge based constraints were more stringent: 8.48 ± 2.65% for the rectum and 4.90 ± 1.98% for the bladder. 19 ± 10 mm single leaf and 1.9 ± 0.7 mm single bank offsets resulted in rectum DVHs worse than 97.7% (2σ) of clinically accepted plans. PTV degradations fell outside of the acceptable range for 0.6 ± 0.3 mm leaf offsets, 0.11 ± 0.06 mm bank offsets, 0.6 ± 1.3 mm of random noise, and 1.0 ± 0.7° of gantry-MLC lag.Utilizing a training set comprised of prior treatment plans, machine learning is used to predict a range of achievable DVHs for the test patient's anatomy. Consequently, degradations leading to statistical outliers may be identified. A knowledge based QA evaluation enables customized QA criteria per treatment site, institution and/or physician and can often be more sensitive to errors than criteria based on organ complication rates.

The clinical impact of detector choice for beam scanning

Recently, the developers of Eclipse have recommended the use of ionization chambers for all profile scanning, including for the modeling of VMAT and stereotactic applications. The purpose of this study is to show the clinical impact caused by the choice of detector with respect to its ability to accurately measure dose in the penumbra and tail regions of a scanned profile. Using scan data acquired with several detectors, including an IBA CC13, a PTW 60012, and a Sun Nuclear EDGE Detector, three complete beam models are created, one for each respective detector. Next, using each beam model, dose volumes are retrospectively recalculated from actual anonymous patient plans. These plans include three full‐arc VMAT prostate plans, three left chest wall plans delivered using irregular compensators, two half‐arc VMAT lung plans, three MLC‐collimated static‐field pairs, and two SBRT liver plans. Finally, plans are reweighted to deliver the same number of monitor units, and mean dose‐to‐target volumes and organs at risk are calculated and compared. Penumbra width did not play a role. Dose in the tail region of the profile made the largest difference. By overresponding in the tail region of the profile, the 60012 diode detector scan data affected the beam model in such a way that target doses were reduced by as much as 0.4% (in comparison to CC13 and EDGE data). This overresponse also resulted in an overestimation of dose to peripheral critical structure, whose dose consisted mainly of scatter. This study shows that, for modeling the 6 MV beam of Acuros XB in Eclipse Version 11, the choice to use a CC13 scanning ion chamber or an EDGE Detector was an unimportant choice, providing nearly identical models in the treatment planning system.

PACS number: 87.55.kh

Direct MC conversion of absorbed dose to graphite to absorbed dose to water for 60Co radiation

The ARPANSA calibration service for (60)Co gamma rays is based on a primary standard graphite calorimeter that measures absorbed dose to graphite. Measurements with the calorimeter are converted to the absorbed dose to water using the calculation of the ratio of the absorbed dose in the calorimeter to the absorbed dose in a water phantom. ARPANSA has recently changed the basis of this calculation from a photon fluence scaling method to a direct Monte Carlo (MC) calculation. The MC conversion uses an EGSnrc model of the cobalt source that has been validated against water tank and graphite phantom measurements, a step that is required to quantify uncertainties in the underlying interaction coefficients in the MC code. A comparison with the Bureau International des Poids et Mesures (BIPM) as part of the key comparison BIPM.RI(I)-K4 showed an agreement of 0.9973 (53).