Dose Distribution Data Research Articles

Minimum dose walking path planning in a nuclear radiation environment based on a modified A* algorithm

An additional cost function for the case with turning points is introduced to the A* algorithm, which can reduce unnecessary turning points. Therefore, the modified A* algorithm consists of three parts: the actual cost function, the estimated cost function, and the turning point cost function. The three functions are normalized so that they can be compared without dimensionality to obtain the optimal path planning in radiation environments. Based on the Geant4 Monte Carlo program, the radiation field model is established and the dose distribution data is obtained. We assign weights to the functions to smooth out the three different cost functions and avoid scenarios in which one function is much greater than the other two. In a small-scale simulation experiment, the path generated by the modified algorithm is consistent with that calculated by the traditional A* method, but the number of turning points is significantly reduced. The modified algorithm takes less time and the cumulative dose on the path is slightly lower. Modified A*, traditional A* and PRM algorithms are used for path planning and comparison in the complex radiation environment of multiple γ sources. The PRM algorithm takes the least computation time to search the path, but the cumulative exposure dose is random. For the path planning of multi-task operation in a radiation field, the cumulative radiation dose of the path planned by the modified A* algorithm is equivalent to that of the traditional A* algorithm, but the modified A* algorithm significantly reduces the number of turning points, the calculation time is significantly shortened, and the execution efficiency is improved. The modified A* algorithm can be used as a reference for path planning for workers in radiation workplaces.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Development of a Relative Dosimetric System for Calibration of 32P Eye Applicators Using Radiochromic Films.

Beta irradiation after bare scleral surgery of primary pterygium is an effective and safe treatment, which reduces the risk of local recurrence. Obtaining the reference dose rate for a radioactive applicator consisting of a plate as a 32P absorber, a steel window and a steel capsule. Relative dosimetry and dose profile were measured using two types of radiochromic films, HD-810 and EBT1, for the 32P applicator and were compared with Monte Carlo simulation data. Dose uniformity in the 32P applicator was obtained with radiochromic HD-810 film. The measurement depth dose distribution data at distances up to 3.8 mm were compared with calculation data, and the values were not found to differ statistically. Depth dose distribution with a large dose gradient was determined and the dose rate data obtained 0.0053 ± 9.9% in unit of Gy/s.mCi at a 0.1 mm depth distance. Practical results indicated that the dose nonuniformity and the maximum symmetrical for the 32P applicator were 11.5% and 9.2%, respectively. Our experiments show that the use of the radiochromic film to perform the relative dosimetric checks is feasible and the activity value with acceptable error can be determined through this indirect method.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Evaluating the effect of low-density inhomogeneities: A comparative study of lithium fluoride detectors, radiochromic films, and collapsed cone algorithm in 6 MV photon beams

In recent years, there has been significant advancement in the development of physical simulators for dose evaluation. Many dosimetric studies employ solid materials, equivalent to human tissues, to evaluate dose distribution. This study aims to evaluate the effectiveness of inhomogeneity correction carried out by the Monaco/Elekta radiotherapy planning software. To achieve this goal, a physical simulator was created using cork boards to simulate lung tissue and solid water to represent other tissues. This simulator was combined with a dosimetric system that utilized lithium fluoride thermoluminescent detectors - RADOS MTS-N (LiF:Mg,Ti). The thermoluminescent detectors were positioned at various depths using a precisely drilled 2.0 mm thick acrylic plate, and they were placed at predefined positions. The irradiation of the simulator was conducted using an Elekta Synergy® Platform accelerator, employing a 6 MV photon beam with a field size of 15 × 15 cm2 and a source-surface distance (SSD) of 97.5 cm. A radiation dose of 200 cGy was applied for this study. In addition to the dosimetric assessment using thermoluminescent detectors, GAFCHROMIC™ EBT-3 Dosimetry Films were utilized to evaluate the dose at the same measurement points. The dose distribution data measured with the detectors were compared with the values provided by the planning system (TPS) and the inhomogeneity correction was verified. The results support the hypothesis that there is a lack of precision in the analytical simulations performed by the TPS, particularly in cases involving dose distribution at interfaces with varying densities.

Implicit neural representation for radiation therapy dose distribution

Objective. Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 106 to 108. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data. Approach. Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function. Main results. Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10−6 and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ∼1 across all the datasets, with a compression ratio of ∼32. Our results also show that model sizes with a bitrate of 1–2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly. Significance. The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

ANALISA DISTRIBUSI DOSIS PADA FANTOM HOMOGEN DAN INHOMOGEN DENGAN SIMULASI MONTE CARLO

Monte Carlo simulation with electron gamma shower (EGSnrc) code can produce 3-dimensional dose distribution data. The percent depth dose curve (PDD), dose profile, and isodose curve can be extracted through this 3-dimensional data. In this study, a photon source with an energy of 2 MeV is placed at the source to surface distance (SSD) from the phantom surface by adjusting the collimator aperture which is used to control the size of the exposure field. The SSD distance was varied at a distance of 50 cm, 70 cm, 80 cm, 90 cm, and 100 cm. The size of the exposure area is also varied by 2 × 2 cm 2 , 5 × 5 cm 2 , 7 × 7 cm 2 , and 10 × 10 cm 2 . The dose distribution analysis was carried out on homogeneous phantoms containing water and inhomogeneous phantoms containing tissue/bone/lung/bone/tissue material. The PDD curve and dose profile represent dose changes with depth and x or y direction, respectively. The PDD curve on a homogeneous phantom shows an increase in the value on the surface of the phantom to the depth with the maximum dose. This curve then decreases gradually with increasing depth. Inhomogeneous ghosts show spikes in the border region of the two media with different densities. This is due to the contribution of the backscattered electrons generated by the bone media and into the tissue media. This phenomenon is also seen in the isodose curve for inhomogeneous phantoms.

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

ASSESSMENT OF RADIATION DOSE OF MOBILE COMPUTED TOMOGRAPHY IN INTENSIVE CARE UNITS.

Intra-hospital transport is associated with fatal risks for the occupants of an intensive care unit (ICU). Thus, mobile CT is used in ICUs. In this study, two-dimensional equivalent dose distribution data were expanded using the inverse square law of distance to identify the potential exposure of radiologic technologists and public and the maximum number of possible daily CT procedures. The exposure dose at 1.5m from the isocentre of the mobile CT was 2.260μSv. Based on the dose limitation (5mSv/yr for controlled area and 1mSv/yr for uncontrolled area) as per National Council on Radiation Protection and Measurement report, the number of possible scans per day was 9 for radiologic technologists and 2 for public. When using the radiation shielding partition with a lead equivalent of 0.3 mmPb, the exposure dose reduced as 0.399μSv. Therefore, mobile CT can be used in ICUs when appropriate shielding is provided.

Application Of Machine Learning For Prediction Of Normal Organ Dose: Feasibility Study In Treatment Planning For Total Marrow Irradiation

Machine learning is an application of artificial intelligence that allows a software system to automatically learn and improve from existing data and make intelligent predictions. We hypothesize that machine learning is applicable in treatment planning for external beam radiotherapy by predicting critical organ dose based on target dose distribution and anatomical data. In this study, we evaluated the feasibility of critical organ dose prediction in treatment planning for total marrow irradiation (TMI). We retrospectively retrieved over 300 historical TMI treatment plans from our clinical database, all of which were generated on treatment planning systems for helical tomotherapy with heterogeneous prescription dose levels. Forty cases were randomly selected as the test dataset, while the other cases were used as the training dataset. Target volumes included skeletal bones, rib cage, lymph nodes, and might optionally include liver, brain, spleen, and testes. Representative dosimetric parameters for over 20 normal organs were used consecutively as the variable for regression modeling. Using the supervised machine learning technique, regression models were built with training feature data that included representative DVH points for targets, anatomical/geometrical parameters of each structure, and values for the variable in the training dataset. Predicted values for the given variable were compared with actual values in the test dataset. Accuracy of the regression models was evaluated with linear regression, correlation analysis, and mean absolute errors (MAE) between predicted and actual values. In the TMI plans, the average bone PTV dose was 16.1±3.6 Gy (range: 9.2 – 21.7 Gy), and the average mean lung dose was 7.3±1.3 Gy (range: 4.2 – 10.0 Gy). Various degrees of correlation were found between dosimetric parameters for the targets and those for the normal organs. Statistically significant correlation (p < 0.05) was found between model predictions and actual plan data for mean dose of the lens, bladder, kidneys, and rectum; however, the Pearson’s correlation coefficient (CE) was < 0.6 or the MAE > 1 Gy. For the average bowel dose, the CE between the predicted and actual values was > 0.75 with statistical significance (p < 0.05) and the MAE < 1 Gy. The CE was > 0.75 with statistical significance (p < 0.05) and the MAE was < 0.7 Gy for most dosimetric parameters of the lung including mean dose, median dose, and D30 to D85 (dose that covers 30 to 85% lung volume), but the MAE was > 1 Gy for dose levels that cover less than 25% of the lung volume. This feasibility study indicated that given representative DVH points for the targets in a TMI plan, the machine learning technique could make predictions for certain dosimetric variables of some normal organs, especially those of the lung, that correlate with actual plan values with statistical significance. This technique can be readily ported to other disease sites and used to aide treatment planning.

Assessment of Different Training Methods in an Artificial Neural Network to Calculate 2D Dose Distribution in Radiotherapy

Introduction: Treatment planning is the most important part of treatment. One of the important entries into treatment planning systems is the beam dose distribution data which maybe typically measured or calculated in a long time. This study aimed at shortening the time of dose calculations using artificial neural network (ANN) and finding the best method of training the ANN using Monte Carlo-N-particle (MCNP5) modeling. Material and Methods: Back-propagation learning algorithm was applied to design the neural network. The ANN was trained by MCNP5 calculations, and different kinds of methods were tested to determine the best method for training. In order to evaluate the accuracy of the ANN, the beam profiles and percentage depth dose (PDD) in the field size of 15×15 cm2 were anticipated by ANN using various training methods. Eventually, the results were compared with those obtained from the MCNP5 code. Results: There were good agreements between the results of comparing MCNP5 calculations with experimental measurements. Among the different training methods, Trainbfg had the least error for calculation of PDD and beam profile. Conclusion: The best training method was found to be Trainbfg, and the results revealed the sufficient accuracy of the modeled ANN.

P209] Determination of the relative dose distribution around an intrabeam electronic brachytherapy source using Fricke gel dosimetry

Purpose The INTRABEAM radiotherapy system is the most commonly used electronic brachytherapy (eBT) system in Europe for cancer treatment. To deliver the prescribed dose, medical physicists use the dose distribution data provided by the manufacturer (Zeiss). This work aims at verifying the correctness of these data for the specific case of breast cancer treatment by comparing the relative dose distribution predicted by Zeiss with that measured using gel dosimetry and that calculated using Monte Carlo simulation. Methods In this work, a Fricke gel dosimeter was used together with MRI readings. This method presents significant advantages as it enables relative dose distribution measurements in 3D, with millimetric spatial resolution. Firstly, the gel response was studied in terms of dose and energy dependence, using several dose levels in different X-ray reference beams. Meanwhile, these irradiation experiments were simulated using the Monte Carlo code Penelope for determining the relative absorbed dose distributions within the gel phantoms and the mean energies of the photon spectra at different depths. Once calibrated, the gel was poured into a dedicated phantom, surrounding the INTRABEAM source associated to a spherical 4 cm diameter applicator, irradiated under clinical conditions. Monte Carlo calculations were then used to convert the absorbed doses to gel into absorbed doses to water, and to obtain the relative absorbed dose to water distribution, required for the comparison. Results As no significant energy dependence was found within the uncertainty limits, the CCRI50b X-ray beam, whose energy spectrum is close to the one of the considered INTRABEAM source, was used to calibrate the gel response in absorbed dose. The polynomial relation obtained was then applied to the measurements with the INTRABEAM system to determine the relative absorbed dose distribution in gel, then converted into a relative dose distribution in water. The predicted, measured and calculated relative dose distributions are in excellent agreement, within the uncertainty limits. Conclusions The agreement obtained between the three methods validates both gel dosimetry as a powerful tool for measuring relative dose distributions delivered by low energy X-ray beams, and the relative dose distribution provided by Zeiss with the INTRABEAM system.

Survey of distribution of seasonal influenza vaccine doses in 201 countries (2004-2015): The 2003 World Health Assembly resolution on seasonal influenza vaccination coverage and the 2009 influenza pandemic have had very little impact on improving influenza control and pandemic preparedness.

There is no global monitoring system for influenza vaccination coverage, making it difficult to assess progress towards the 2003 World Health Assembly (WHA) vaccination coverage target. In 2008, the IFPMA Influenza Vaccine Supply International Task Force (IVS) developed a survey method to assess the global distribution of influenza vaccine doses as a proxy for vaccination coverage rates. The latest dose distribution data for 2014 and 2015 was used to update previous analyses. Data were confidentially collected and aggregated by the IFPMA Secretariat, and combined with previous IFPMA IVS survey data (2004-2013). Data were available from 201 countries over the 2004-2015 period. A "hurdle" rate was defined as the number of doses required to reach 15.9% of the population in 2008. Overall, the number of distributed doses progressively increased between 2004 and 2011, driven by a 150% increase in AMRO, then plateaued. One percent fewer doses were distributed in 2015 than in 2011. Twenty-three countries were above the hurdle rate in 2015, compared to 15 in 2004, but distribution was highly uneven in and across all WHO regions. Three WHO regions (AMRO, EURO and WPRO) accounted for about 95% of doses distributed. But in EURO and WPRO, distribution rates in 2015 were only marginally higher than in 2004, and in EURO there was an overall downward trend in dose distribution. The vast majority of countries cannot meet the 2003WHA coverage targets and are inadequately prepared for a global influenza pandemic. With only 5% of influenza vaccine doses being distributed to 50% of the world's population, there is urgency to redress the gross inequities in disease prevention and in pandemic preparedness. The 2003WHA resolution must be reviewed and revised and a call issued for the renewed commitment of Member States to influenza vaccination coverage targets.

Low dose out-of-field radiotherapy, part 2: Calculating the mean photon energy values for the out-of-field photon energy spectrum from scattered radiation using Monte Carlo methods

PurposeDuring radiotherapy, leakage from the machine head and collimator expose patients to out-of-field irradiation doses, which may cause secondary cancers. To quantify the risks of secondary cancers due to out-of-field doses, it is first necessary to measure these doses. Since most dosimeters are energy-dependent, it is essential to first determine the type of photon energy spectrum in the out-of-field area. The aim of this study was to determine the mean photon energy values for the out-of-field photon energy spectrum for a 6 MV photon beam using the GEANT 4–Monte Carlo method. Material and methodsA specially-designed large water phantom was simulated with a static field at gantry 0°. The source-to-surface distance was 92cm for an open field size of 10×10cm2. The photon energy spectra were calculated at five unique positions (at depths of 0.5, 1.6, 4, 6, 8, and 10cm) along the central beam axis and at six different off-axis distances. ResultsMonte Carlo simulations showed that mean radiation energy levels drop rapidly beyond the edge of the 6 MV photon beam field: at a distance of 10cm, the mean energy level is close to 0.3MeV versus 1.5MeV at the central beam axis. In some cases, the energy level actually increased even as the distance from the field edge increased: at a depth of 1.6cm and 15cm off-axis, the mean energy level was 0.205MeV versus 0.252MeV at 20cm off-axis. ConclusionThe out-of-field energy spectra and dose distribution data obtained in this study with Monte Carlo methods can be used to calibrate dosimeters to measure out-of-field radiation from 6MV photons.

SU‐G‐206‐14: Individual Dose Alert Thresholds for Abdominal CT Protocols Due to Statistical Independence of Dose Distributions

Purpose:To determine whether a single dose management system alert threshold will suffice for the 34 abdomen protocols on our institution's CT scanners, or if these alert thresholds should be set per protocol. Parametric statistical analysis is ideally valid only for distributions demonstrating: (1) normality (2) homogeneity of variance (3) interval scale and (4) independence. Dose distributions are notoriously non‐normal and thus nonparametric statistical analysis is typically required.Methods:Dose distribution data were collected from a HD750 CT scanner (GE Healthcare) at our institution over the third and fourth quarters of 2015. In total there were 1793 abdomen studies using 34 different protocols. Protocols were selected for statistical analysis (SPSS, IBM Corp.) if the count over the period was ≥ 50 to produce statistically meaningful results. Nine protocols with count ranging from 61‐398 were selected. Protocol dose distributions were statistically analyzed to determine: (1) normality – Shapiro‐Wilk test (2) homogeneity of variance – Levene Statistic (3) similarity of medians – Median test, and (4) similarity of distribution – Kruskal‐Wallis test.Results:For the nine abdomen protocols used with exam count ≥ 50, the dose distributions were statistically significantly different from normal for eight protocols (p≤[0.001‐0.035]), homogeneity of variance invalidated for five protocols (p≤[0.001‐0.037]), median values not the same across all protocols (p&lt;0.001), and all dose distributions different from each other (p&lt;0.001).Conclusion:That all abdomen protocols with exam count ≥ 50, the distributions were predominantly: (1) non‐normal (2) of unequal variance over the distributions (3) of unequal means and (4) non‐identical distributions indicates that the abdomen protocols in general must be treated independently with non‐parametric statistical analysis and thus the alert thresholds should be calculated and set individually, that is, there cannot be a solitary meaningful alert threshold utilized over all abdominal protocols examined in this study.

Calculation of size specific dose estimates (SSDE) value at cylindrical phantom from CBCT Varian OBI v1.4 X-ray tube EGSnrc Monte Carlo simulation based

The aim of this research was to calculate Size Specific Dose Estimates (SSDE) generated by the varian OBI CBCT v1.4 X-ray tube working at 100 kV using EGSnrc Monte Carlo simulations. The EGSnrc Monte Carlo code used in this simulation was divided into two parts. Phase space file data resulted by the first part simulation became an input to the second part. This research was performed with varying phantom diameters of 5 to 35 cm and varying phantom lengths of 10 to 25 cm. Dose distribution data were used to calculate SSDE values using trapezoidal rule (trapz) function in a Matlab program. SSDE obtained from this calculation was compared to that in AAPM report and experimental data. It was obtained that the normalization of SSDE value for each phantom diameter was between 1.00 and 3.19. The normalization of SSDE value for each phantom length was between 0.96 and 1.07. The statistical error in this simulation was 4.98% for varying phantom diameters and 5.20% for varying phantom lengths. This study demonstrated the accuracy of the Monte Carlo technique in simulating the dose calculation. In the future, the influence of cylindrical phantom material to SSDE would be studied.

Risk assessment of peanut protein traces found in refined peanut oil

Highly refined peanut oil is considered to pose a risk to people with peanut allergy.[1] Although that risk has not been characterised, but controlled clinical challenges suggest it is negligible. A consequence is that the risk from cross contact between other refined vegetable oils and refined peanut oil during production must be assessed in order to assure consumer safety. Use of these refined vegetable oils in consumer food products potentially leads to very small amounts of peanut proteins in the final product. However, it is not clear yet if these amounts could reach levels that are relevant for public health. Allergen risk assessment using probabilistic techniques enables quantitative estimation of the risk after the consumption of a product that unintendedly contains an allergen. Several scenarios were defined for the unintended allergen levels in food products based on the estimated production scale of refined peanut oil in the UK, estimated figures for cross-contact and the peanut protein concentration in refined peanut oil. The consumption of 12 food products, selected for their high contribution to fat consumption including biscuits, margarine, ice cream and fried food, was combined with the peanut dose distribution. Food consumption data were derived from the National Diet and Nutrition Survey in the UK, and the Dutch National Food Consumption Survey. Peanut dosedistribution data were the same as used for the scientific review of VITAL® (Voluntary Incidental Trace Allergen Labelling).[2] We estimated that the amounts and distributions of peanut proteins posed a risk of objective allergic reactions in 0.03 to 0.6% of the peanut-allergic consumer population. The concentrations of peanut protein in selected food products would be below the limit of detection of current analytical methods and in all the scenarios examined, which included worst-case, the amounts consumed were below the VITAL reference dose for peanut. On that basis, the health risk is very small and none of those products would warrant precautionary labelling.

Calculation of dose distributions for 12 106Ru/106Rh ophthalmic applicator models with the PENELOPE Monte Carlo code

For decades, (106)Ru/(106)Rh ophthalmic applicators have been widely used to treat intraocular malignancies with good clinical outcomes. Determining the dosimetry of such applicators is challenging because of their spherical shape, the low range of emitted betas and the steep gradients involved. Furthermore, there is a lack of dose distribution data in the literature for some applicator models. It is crucial to understand the generated dose distributions to improve the treatment planning process. This study aims to calculate the dose distributions in water for 12 commercially available (106)Ru/(106)Rh ophthalmic applicators. The applicators were modeled using the Monte Carlo (MC) code PENELOPE-2008.1. A planar nonclinical applicator was also simulated. Depth-dose profiles along the central axis and lateral dose profiles at several depths were calculated and compared with the available published measured and calculated data. In the absence of published data, measurements performed by the manufacturer were used. For some applicators, the calculated results obtained in this work showed improved consistency with the available experimental data compared with previously published calculations. For others, the data calculated in this work represent the first published dose data. The MC code PENELOPE-2008.1 can accurately calculate the dose distributions for (106)Ru/(106)Rh ophthalmic applicators. The obtained set of tabulated dose distribution values may be useful for quality assurance of treatment planning systems and as a reference for comparison with future measurements and calculations.

線量分布解析におけるラジオクロミックフィルムドジメトリーの正確性の向上に関する取り組み─フラットベッドスキャナの影響の補正法と色成分を利用した補正法の組み合わせ─

It has been reported that the light scattering could worsen the accuracy of dose distribution measurement using a radiochromic film. The purpose of this study was to investigate the accuracy of two different films, EDR2 and EBT2, as film dosimetry tools. The effectiveness of a correction method for the non-uniformity caused from EBT2 film and the light scattering was also evaluated. In addition the efficacy of this correction method integrated with the red/blue correction method was assessed. EDR2 and EBT2 films were read using a flatbed charge-coupled device scanner (EPSON 10000G). Dose differences on the axis perpendicular to the scanner lamp movement axis were within 1% with EDR2, but exceeded 3% (Maximum: +8%) with EBT2. The non-uniformity correction method, after a single film exposure, was applied to the readout of the films. A corrected dose distribution data was subsequently created. The correction method showed more than 10%-better pass ratios in dose difference evaluation than when the correction method was not applied. The red/blue correction method resulted in 5%-improvement compared with the standard procedure that employed red color only. The correction method with EBT2 proved to be able to rapidly correct non-uniformity, and has potential for routine clinical IMRT dose verification if the accuracy of EBT2 is required to be similar to that of EDR2. The use of red/blue correction method may improve the accuracy, but we recommend we should use the red/blue correction method carefully and understand the characteristics of EBT2 for red color only and the red/blue correction method.

SU-E-T-37: Evaluation of Software Systems That Estimate Patient Dose Errors Based on Planar IMRT QA Measurements

Purpose: Although dose evaluation in IMRT QA may more closely depict patient dosimetry, measurements are in a surrogate phantom - not in the patient's anatomy. Techniques and software systems are commercially available which use individual field dosimetry measurements as input into algorithms that estimate 3D patient dose distributions. These dose distribution data when overlaid on CT scan derived target volumes and OARs, enables direct DVH analysis vs. TPS DVH. The purpose of this work is to present a systematic benchmarking technique for such kind of software systems. Methods: A MapCheck2 diode array and 3DVH software from Sun Nuclear were used for this study. Benchmarking consists of taking IMRT beams created in actual patient treatment plans; re-computing these on a flat phantom using in the RTPS, and exporting these calculations to 3DVH as individual patient plans. Accuracy of the output of 3DVH is tested by comparing measured planar doses over a range of depths to the same planes reconstructed by 3DVH. Results: The Gamma Index evaluation for comparing calculated 3D dose with measured 3D dose with 2% and 2mm DTA criteria returned a pass rate of > 90% for all patient cases calculated by the treatment planning system and it returned a pass rate of > 96% in 9 out of 10 cases calculated by 3DVH. Extracted computed dose planes with 3DVH software at different depths in the flat phantom passed all gamma evaluation analyses when compared to measured planes at different depths using MapCheck2. Conclusions: It was shown that the 3D dose distribution predicted by the 3DVH software is both accurate and consistent. Accuracy is shown by the agreement of the calculations with measurements at different depths. Consistency is shown by using as inputs measured planar dose at different depths and comparing the results.