bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH‐C‐12A‐10: Surface Dose Enhancement Using Novel Hybrid Electron and Photon Low‐Z Therapy Beams: Monte Carlo Simulation
Authors: Parsons C , Parsons D , Robar J , Kelly R 
Published: July 2014

Purpose:The introduction of the TrueBeam linac platform provides access to an in‐air target assembly making it possible to apply novel treatments using multiple target designs. One such novel treatment uses multiple low‐Z targets to enhance surface dose replacing the use of synthetic tissue equivalent material (bolus). This treatment technique will decrease the common dosimetric and set up errors prevalent in using physical treatment accessories like bolus. The groundwork for a novel treatment beam used to enhance surface dose to within 80‐100% of the dose at dmax by utilizing low‐Z (Carbon) targets of various percent CSDA range thickness operated at 2.5–4 MeV used in conjunction with a clinical 6 MV beam is presented herein.Methods:A standard Monte Carlo model of a Varian Clinac accelerator was developed to manufacturers specifications. Simulations were performed using Be, C, AL, and C, as potential low‐Z targets, placed in the secondary target position. The results determined C to be the target material of choice. Simulations of 15, 30 and 60% CSDA range C beams were propagated through slab phantoms. The resulting PDDs were weighted and combined with a standard 6 MV treatment beam. Versions of the experimental targets were installed into a 2100C Clinac and the models were validated.Results:Carbon was shown to be the low‐Z material of choice for this project. Using combinations of 15, 30, 60% CSDA beams operated at 2.5 and 4 MeV in combination with a standard 6 MV treatment beam the surface dose was shown to be enhanced to within 80–100% the dose at dmax.Conclusion:The modeled low‐Z beams were successfully validated using machined versions of the targets. Water phantom measurements and slab phantom simulations show excellent correlation. Patient simulations are now underway to compare the use of bolus with the proposed novel beams.NSERC
TH‐E‐BRE‐02: A Forward Scattering Approximation to Dose Calculation Using the Linear Boltzmann Transport Equation
Authors: Catt B , Snyder M 
Published: July 2014

Purpose:To investigate the use of the linear Boltzmann transport equation as a dose calculation tool which can account for interface effects, while still having faster computation times than Monte Carlo methods. In particular, we introduce a forward scattering approximation, in hopes of improving calculation time without a significant hindrance to accuracy.Methods:Two coupled Boltzmann transport equations were constructed, one representing the fluence of photons within the medium, and the other, the fluence of electrons. We neglect the scattering term within the electron transport equation, resulting in an extreme forward scattering approximation to reduce computational complexity. These equations were then solved using a numerical technique for solving partial differential equations, known as a finite difference scheme, where the fluence at each discrete point in space is calculated based on the fluence at the previous point in the particle's path. Using this scheme, it is possible to develop a solution to the Boltzmann transport equations by beginning with boundary conditions and iterating across the entire medium. The fluence of electrons can then be used to find the dose at any point within the medium.Results:Comparisons with Monte Carlo simulations indicate that even simplistic techniques for solving the linear Boltzmann transport equation yield expected interface effects, which many popular dose calculation algorithms are not capable of predicting. Implementation of a forward scattering approximation does not appear to drastically reduce the accuracy of this algorithm.Conclusion:Optimized implementations of this algorithm have been shown to be very accurate when compared with Monte Carlo simulations, even in build up regions where many models fail. Use of a forward scattering approximation could potentially give a reasonably accurate dose distribution in a shorter amount of time for situations where a completely accurate dose distribution is not required, such as in certain optimization algorithms.
TH‐E‐BRE‐03: A Novel Method to Account for Ion Chamber Volume Averaging Effect in a Commercial Treatment Planning System Through Convolution
Authors: Barraclough B , Li J , Liu C , Yan G 
Published: July 2014

Purpose:Fourier‐based deconvolution approaches used to eliminate ion chamber volume averaging effect (VAE) suffer from measurement noise. This work aims to investigate a novel method to account for ion chamber VAE through convolution in a commercial treatment planning system (TPS).Methods:Beam profiles of various field sizes and depths of an Elekta Synergy were collected with a finite size ion chamber (CC13) to derive a clinically acceptable beam model for a commercial TPS (Pinnacle3), following the vendor‐recommended modeling process. The TPS‐calculated profiles were then externally convolved with a Gaussian function representing the chamber (σ = chamber radius). The agreement between the convolved profiles and measured profiles was evaluated with a one dimensional Gamma analysis (1%/1mm) as an objective function for optimization. TPS beam model parameters for focal and extra‐focal sources were optimized and loaded back into the TPS for new calculation. This process was repeated until the objective function converged using a Simplex optimization method. Planar dose of 30 IMRT beams were calculated with both the clinical and the re‐optimized beam models and compared with MapCHEC™ measurements to evaluate the new beam model.Results:After re‐optimization, the two orthogonal source sizes for the focal source reduced from 0.20/0.16 cm to 0.01/0.01 cm, which were the minimal allowed values in Pinnacle. No significant change in the parameters for the extra‐focal source was observed. With the re‐optimized beam model, average Gamma passing rate for the 30 IMRT beams increased from 92.1% to 99.5% with a 3%/3mm criterion and from 82.6% to 97.2% with a 2%/2mm criterion.Conclusion:We proposed a novel method to account for ion chamber VAE in a commercial TPS through convolution. The reoptimized beam model, with VAE accounted for through a reliable and easy‐to‐implement convolution and optimization approach, outperforms the original beam model in standard IMRT QA verification.
TH‐E‐BRE‐04: An Online Replanning Algorithm for VMAT
Authors: Ahunbay E , Moreau M , Li X 
Published: July 2014

Purpose:To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filtered (FF) and flattening filter free (FFF) beams.Methods:A software tool was developed to interface with a VMAT planning system ((Monaco, Elekta), enabling the output of detailed beam/machine parameters of original VMAT plans generated based on planning CTs for FF or FFF beams. A SAM algorithm, previously developed for fixed‐beam IMRT, was modified to allow the algorithm to correct for interfractional variations (e.g., setup error, organ motion and deformation) by morphing apertures based on the geometric relationship between the beam's eye view of the anatomy from the planning CT and that from the daily CT for each control point. The algorithm was tested using daily CTs acquired using an in‐room CT during daily IGRT for representative prostate cancer cases along with their planning CTs. The algorithm allows for restricted MLC leaf travel distance between control points of the VMAT delivery to prevent SAM from increasing leaf travel, and therefore treatment delivery time.Results:The VMAT plans adapted to the daily CT by SAM were found to improve the dosimetry relative to the IGRT repositioning plans for both FF and FFF beams. For the adaptive plans, the changes in leaf travel distance between control points were < 1cm for 80% of the control points with no restriction. When restricted to the original plans' maximum travel distance, the dosimetric effect was minimal. The adaptive plans were delivered successfully with similar delivery times as the original plans. The execution of the SAM algorithm was < 10 seconds.Conclusion:The SAM algorithm can quickly generate deliverable online‐adaptive VMAT plans based on the anatomy of the day for both FF and FFF beams.
TH‐E‐BRE‐07: Development of Dose Calculation Error Predictors for a Widely Implemented Clinical Algorithm
Authors: Egan A , Laub W 
Published: July 2014

Purpose:Several shortcomings of the current implementation of the analytic anisotropic algorithm (AAA) may lead to dose calculation errors in highly modulated treatments delivered to highly heterogeneous geometries. Here we introduce a set of dosimetric error predictors that can be applied to a clinical treatment plan and patient geometry in order to identify high risk plans. Once a problematic plan is identified, the treatment can be recalculated with more accurate algorithm in order to better assess its viability.Methods:Here we focus on three distinct sources dosimetric error in the AAA algorithm. First, due to a combination of discrepancies in smallfield beam modeling as well as volume averaging effects, dose calculated through small MLC apertures can be underestimated, while that behind small MLC blocks can overestimated. Second, due the rectilinear scaling of the Monte Carlo generated pencil beam kernel, energy is not properly transported through heterogeneities near, but not impeding, the central axis of the beamlet. And third, AAA overestimates dose in regions very low density (< 0.2 g/cm3). We have developed an algorithm to detect the location and magnitude of each scenario within the patient geometry, namely the field‐size index (FSI), the heterogeneous scatter index (HSI), and the lowdensity index (LDI) respectively.Results:Error indices successfully identify deviations between AAA and Monte Carlo dose distributions in simple phantom geometries. Algorithms are currently implemented in the MATLAB computing environment and are able to run on a typical RapidArc head & neck geometry in less than an hour.Conclusion:Because these error indices successfully identify each type of error in contrived cases, with sufficient benchmarking, this method can be developed into a clinical tool that may be able to help estimate AAA dose calculation errors and when it might be advisable to use Monte Carlo calculations.
TH‐E‐BRE‐09: TrueBeam Monte Carlo Absolute Dose Calculations Using Monitor Chamber Backscatter Simulations and Linac‐Logged Target Current
Authors: A Popescu I , Lobo J , Sawkey D , Svatos M 
Published: July 2014

Purpose:To simulate and measure radiation backscattered into the monitor chamber of a TrueBeam linac; establish a rigorous framework for absolute dose calculations for TrueBeam Monte Carlo (MC) simulations through a novel approach, taking into account the backscattered radiation and the actual machine output during beam delivery; improve agreement between measured and simulated relative output factors.Methods:The ‘monitor backscatter factor’ is an essential ingredient of a well‐established MC absolute dose formalism (the MC equivalent of the TG‐51 protocol). This quantity was determined for the 6 MV, 6X FFF, and 10X FFF beams by two independent Methods: (1) MC simulations in the monitor chamber of the TrueBeam linac; (2) linac‐generated beam record data for target current, logged for each beam delivery. Upper head MC simulations used a freelyavailable manufacturer‐provided interface to a cloud‐based platform, allowing use of the same head model as that used to generate the publicly‐available TrueBeam phase spaces, without revealing the upper head design. The MC absolute dose formalism was expanded to allow direct use of target current data.Results:The relation between backscatter, number of electrons incident on the target for one monitor unit, and MC absolute dose was analyzed for open fields, as well as a jaw‐tracking VMAT plan. The agreement between the two methods was better than 0.15%. It was demonstrated that the agreement between measured and simulated relative output factors improves across all field sizes when backscatter is taken into account.Conclusion:For the first time, simulated monitor chamber dose and measured target current for an actual TrueBeam linac were incorporated in the MC absolute dose formalism. In conjunction with the use of MC inputs generated from post‐delivery trajectory‐log files, the present method allows accurate MC dose calculations, without resorting to any of the simplifying assumptions previously made in the TrueBeam MC literature. This work has been partially funded by Varian Medical Systems.
TH‐E‐BRF‐02: 4D‐CT Ventilation Image‐Based IMRT Plans Are Dosimetrically Comparable to SPECT Ventilation Image‐Based Plans
Authors: Kida S , Bal M , Kabus S , Loo B , Keall P , Yamamoto T 
Published: July 2014

Purpose:An emerging lung ventilation imaging method based on 4D‐CT can be used in radiotherapy to selectively avoid irradiating highly‐functional lung regions, which may reduce pulmonary toxicity. Efforts to validate 4DCT ventilation imaging have been focused on comparison with other imaging modalities including SPECT and xenon CT. The purpose of this study was to compare 4D‐CT ventilation image‐based functional IMRT plans with SPECT ventilation image‐based plans as reference.Methods:4D‐CT and SPECT ventilation scans were acquired for five thoracic cancer patients in an IRB‐approved prospective clinical trial. The ventilation images were created by quantitative analysis of regional volume changes (a surrogate for ventilation) using deformable image registration of the 4D‐CT images. A pair of 4D‐CT ventilation and SPECT ventilation image‐based IMRT plans was created for each patient. Regional ventilation information was incorporated into lung dose‐volume objectives for IMRT optimization by assigning different weights on a voxel‐by‐voxel basis. The objectives and constraints of the other structures in the plan were kept identical. The differences in the dose‐volume metrics have been evaluated and tested by a paired t‐test. SPECT ventilation was used to calculate the lung functional dose‐volume metrics (i.e., mean dose, V20 and effective dose) for both 4D‐CT ventilation image‐based and SPECT ventilation image‐based plans.Results:Overall there were no statistically significant differences in any dose‐volume metrics between the 4D‐CT and SPECT ventilation imagebased plans. For example, the average functional mean lung dose of the 4D‐CT plans was 26.1±9.15 (Gy), which was comparable to 25.2±8.60 (Gy) of the SPECT plans (p = 0.89). For other critical organs and PTV, nonsignificant differences were found as well.Conclusion:This study has demonstrated that 4D‐CT ventilation image‐based functional IMRT plans are dosimetrically comparable to SPECT ventilation image‐based plans, providing evidence to use 4D‐CT ventilation imaging for clinical applications.Supported in part by Free to Breathe Young Investigator Research Grant and NIH/NCI R01 CA 093626. The authors thank Philips Radiation Oncology Systems for the Pinnacle3 treatment planning systems
TH‐E‐BRF‐04: Characterizing the Response of Texture‐Based CT Image Features for Quantification of Radiation‐Induced Normal Lung Damage
Authors: Krafft S , Court L , Briere T , Martel M 
Published: July 2014

Purpose:Radiation induced lung damage (RILD) is an important dose‐limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time‐related response for texture‐based lung CT image features that serve as potential quantitative measures of RILD.Methods:Pre‐ and post‐RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre‐RT and available follow‐up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture‐based features (including co‐occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre‐RT), planned dose and time post‐RT.Results:Seventy‐three follow‐up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features.Conclusion:Characterizing the dose and time‐related response of a large number of texture‐based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.
TH‐E‐BRF‐05: Comparison of Survival‐Time Prediction Models After Radiotherapy for High‐Grade Glioma Patients Based On Clinical and DVH Features
Authors: Magome T , Haga A , Igaki H , Sekiya N , Masutani Y , Sakumi A , Mukasa A , Nakagawa K 
Published: July 2014

Purpose:Although many outcome prediction models based on dose‐volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high‐grade glioma patients based on features including clinical and dose‐volume histogram (DVH) information.Methods:A total of 35 patients with high‐grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step‐wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R2) between the predicted and actual survival time for the training and test dataset.Results:In the multiple regression analysis, the value of R2 between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R2 was 0.806 for the training dataset and 0.811 for the test dataset.Conclusion:Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high‐grade glioma.This work was partly supported by the JSPS Core‐to‐Core Program(No. 23003) and Grant‐in‐aid from the JSPS Fellows.
TH‐E‐BRF‐07: Raman Spectroscopy for Radiation Treatment Response Assessment in a Lung Metastases Mouse Model
Authors: Devpura S , Barton K , Brown S , Sethi S , Klein M , Siddiqui F , Chetty I 
Published: July 2014

Purpose:Raman spectroscopy is an optical spectroscopic method used to probe chemical information about a target tissue. Our goal was to investigate whether Raman spectroscopy is able to distinguish lung tumors from normal lung tissue and whether this technique can identify the molecular changes induced by radiation.Methods:4T1 mouse breast cancer cells were implanted subcutaneously into the flanks of 6 Balb/C female mice. Four additional mice were used as “normal lung” controls. After 14 days, 3 mice bearing tumors received 6Gy to the left lung with 6MV photons and the other three were treated as “unirradiated tumor” controls. At a 24‐hour time point, lungs were excised and the specimens were sectioned using a cryostat; alternating sections were either stained with hematoxylin and eosin (H&E) for evaluation by a pathologist or unstained for Raman measurements. 240 total Raman spectra were collected; 84 from normal lung controls; 63 from unirradiated tumors and 64 from tumors irradiated with 6Gy in a single fraction. Raman spectra were also collected from normal lung tissues of mice with unirradiated tumors. Principal component analysis (PCA) and discriminant function analysis (DFA) were performed to analyze the data.Results:Raman bands assignable to DNA/RNA showed prominent contributions in tumor tissues while Raman bands associated with hemoglobin showed strong contributions in normal lung tissue. PCA/DFA analysis identified normal lung tissue and tumor with 100% and 98.4% accuracy, respectively, relative to pathologic scoring. Additionally, normal lung tissues from unirradiated mice bearing tumors were classified as normal with 100% accuracy. In a model consisting of unirradiated and irradiated tumors identification accuracy was 79.4% and 93.8% respectively, relative to pathologic assessment.Conclusion:Initial results demonstrate the promise for Raman spectroscopy in the diagnosis normal vs. lung metastases as well as the assessment of radiation therapy response. Further investigations are needed to validate these results.This study was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA