Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification

PURPOSE: To determine the implications of the use of the Anisotropic Analytical Algorithm(AAA) for the production and dosimetric verification of IMRT plans for treatments of the prostate, parotid, nasopharynx and lung. METHODS: 72 IMRT treatment plans produced using the Pencil Beam Convolution (PBC)algorithm were recalculated using the AAA and the dose distributions compared. 24 of the plans were delivered to inhomogeneous phantoms and verification measurements made using a pinpoint ionisation chamber. The agreement between the AAA and measurement was determined. RESULTS: Small differences were seen in the prostate plans, with the AAA predicting slightly lower minimum PTV doses. In the parotid plans, there were small increases in the lens and contralateral parotid doses while the nasopharyngeal plans revealed a reduction in the volume of the PTV covered by the 95% isodose (the V95%) when the AAA was used. Large changes were seen in the lung plans, the AAA predicting reductions in the minimum PTV dose and large reductions in the V95%. The AAA also predicted small increases in the mean dose to the normal lung and the V20. In the verification measurements, all AAA calculations were within 3% or 3.5mm distance to agreement of the measured doses. Conclusions: The AAA should be used in preference to the PBC algorithm for treatments involving low density tissue but this may necessitate re-evaluation of plan acceptability criteria. Improvements to the Multi-Resolution Dose Calculation algorithm used in the inverse planning are required to reduce the convergence error in the presence of lung tissue. There was excellent agreement between the AAA and verification measurements for all sites

Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification

PURPOSE: To determine the implications of the use of the Anisotropic Analytical Algorithm(AAA) for the production and dosimetric verification of IMRT plans for treatments of the prostate, parotid, nasopharynx and lung. METHODS: 72 IMRT treatment plans produced using the Pencil Beam Convolution (PBC)algorithm were recalculated using the AAA and the dose distributions compared. 24 of the plans were delivered to inhomogeneous phantoms and verification measurements made using a pinpoint ionisation chamber. The agreement between the AAA and measurement was determined. RESULTS: Small differences were seen in the prostate plans, with the AAA predicting slightly lower minimum PTV doses. In the parotid plans, there were small increases in the lens and contralateral parotid doses while the nasopharyngeal plans revealed a reduction in the volume of the PTV covered by the 95% isodose (the V95%) when the AAA was used. Large changes were seen in the lung plans, the AAA predicting reductions in the minimum PTV dose and large reductions in the V95%. The AAA also predicted small increases in the mean dose to the normal lung and the V20. In the verification measurements, all AAA calculations were within 3% or 3.5mm distance to agreement of the measured doses. Conclusions: The AAA should be used in preference to the PBC algorithm for treatments involving low density tissue but this may necessitate re-evaluation of plan acceptability criteria. Improvements to the Multi-Resolution Dose Calculation algorithm used in the inverse planning are required to reduce the convergence error in the presence of lung tissue. There was excellent agreement between the AAA and verification measurements for all sites

A Monte Carlo based phase space model for quality assurance of intensity modulated radiotherapy incorporating leaf specific characteristics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134797/1/mp3409.pd

COMPREHENSIVE CALCULATION-BASED IMRT QA USING R&V DATA, TREATMENT RECORDS, AND A SECOND TREATMENT PLANNING SYSTEM

Purpose: Traditional patient-specific IMRT QA measurements are labor intensive and consume machine time. Calculation-based IMRT QA methods typically are not comprehensive. We have developed a comprehensive calculation-based IMRT QA method to detect uncertainties introduced by the initial dose calculation, the data transfer through the Record-and-Verify (R&V) system, and various aspects of the physical delivery. Methods: We recomputed the treatment plans in the patient geometry for 48 cases using data from the R&V, and from the delivery unit to calculate the “as-transferred” and “as-delivered” doses respectively. These data were sent to the original TPS to verify transfer and delivery or to a second TPS to verify the original calculation. For each dataset we examined the dose computed from the R&V record (RV) and from the delivery records (Tx), and the dose computed with a second verification TPS (vTPS). Each verification dose was compared to the clinical dose distribution using 3D gamma analysis and by comparison of mean dose and ROI-specific dose levels to target volumes. Plans were also compared to IMRT QA absolute and relative dose measurements. Results: The average 3D gamma passing percentages using 3%-3mm, 2%-2mm, and 1%-1mm criteria for the RV plan were 100.0 (σ=0.0), 100.0 (σ=0.0), and 100.0 (σ=0.1); for the Tx plan they were 100.0 (σ=0.0), 100.0 (σ=0.0), and 99.0 (σ=1.4); and for the vTPS plan they were 99.3 (σ=0.6), 97.2 (σ=1.5), and 79.0 (σ=8.6). When comparing target volume doses in the RV, Tx, and vTPS plans to the clinical plans, the average ratios of ROI mean doses were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.990 (σ=0.009) and ROI-specific dose levels were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.980 (σ=0.043), respectively. Comparing the clinical, RV, TR, and vTPS calculated doses to the IMRT QA measurements for all 48 patients, the average ratios for absolute doses were 0.999 (σ=0.013), 0.998 (σ=0.013), 0.999 σ=0.015), and 0.990 (σ=0.012), respectively, and the average 2D gamma(5%-3mm) passing percentages for relative doses for 9 patients was were 99.36 (σ=0.68), 99.50 (σ=0.49), 99.13 (σ=0.84), and 98.76 (σ=1.66), respectively. Conclusions: Together with mechanical and dosimetric QA, our calculation-based IMRT QA method promises to minimize the need for patient-specific QA measurements by identifying outliers in need of further review

eIMRT: a web platform for the verification and optimization of radiation treatment plans

The eIMRT platform is a remote distributed computing tool that provides users with Internet access to three different services: Monte Carlo optimization of treatment plans, CRT & IMRT treatment optimization, and a database of relevant radiation treatments/clinical cases. These services are accessible through a user-friendly and platform independent web page. Its flexible and scalable design focuses on providing the final users with services rather than a collection of software pieces. All input and output data (CT, contours, treatment plans and dose distributions) are handled using the DICOM format. The design, implementation, and support of the verification and optimization algorithms are hidden to the user. This allows a unified, robust handling of the software and hardware that enables these computation-intensive services. The eIMRT platform is currently hosted by the Galician Supercomputing Center (CESGA) and may be accessible upon request (there is a demo version at http://eimrt.cesga.es:8080/ eIMRT2/demo; request access in http://eimrt.cesga.es/signup.html). This paper describes all aspects of the eIMRT algorithms in depth, its user interface, and its services. Due to the flexible design of the platform, it has numerous applications including the intercenter comparison of treatment planning, the quality assurance of radiation treatments, the design and implementation of new approaches to certain types of treatments, and the sharing of information on radiation treatment techniques. In addition, the web platform and software tools developed for treatment verification and optimization have a modular design that allows the user to extend them with new algorithms. This software is not a commercial product. It is the result of the collaborative effort of different public research institutions and is planned to be distributed as an open source project. In this way, it will be available to any user; new releases will be generated with the new implemented codes or upgradesThis work was financed by Xunta de Galicia of Spain through grant PGIDT05SIN00101CT and by the European Community through the BeInGrid projectS

Higher Statistical Uncertainty With Small Pixel Sizes Gives Higher Pass Rates.

Monte Carlo (MC) based dose calculation methods trade-off accuracy at the expense of computational time, which is, correlated to the user input values of statistical uncertainty and pixel spacing (1). It was first hinted by low et. al. that noise generated within either the calculated or measured plan distributions can affect the result of the plan verification by method of ‘Gamma Index Analysis’(GI) (2). The purpose of this research experiment is to investigate a possible correlation between added noise from increasing MC statistical uncertainty and increasing the odds of a plan passing the GI verification criteria. For this research experiment, we calculated 10 head and neck radiation therapy treatment plans using the MC dose calculation method within Monaco TPS. We varied the statistical uncertainty values from 5%, 3%, 1% and 0.25% and varied the voxel size values from 3mm, 2mm and 1mm. The treatment plans were then administered on an Elekta Versa linear accelerator and measured using Mapcheck dose measurement device. Each plan was evaluated for clinical pass/fail using the GI Analysis with criteria 3%/3mm and 2%/2mm. For 1 mm voxel size, 3%/3mm GI, there was an increase in average gamma pass rates from 98.91% calculated at 0.5% statistical uncertainty to 99.61% calculated at 5% statistical uncertainty. For 1 mm voxel size, 2%/2mm GI, there was an increase in average gamma pass rates from 97.02% calculated at 0.5% statistical uncertainty to 98.80% calculated at 5% statistical uncertainty. At 2 mm and 3 mm voxel sizes, there was not a clear demonstrable increase in average gamma pass rates. The experimental results conclude that the user must be careful when selecting a statistical uncertainty prior to performing a MC dose calculation. The input of a high statistical uncertainty does not lead to more points failing the GI, but paradoxically, can increase the chances that the evaluated radiation therapy plan will pass the acceptance evaluation

3D VMAT Verification Based on Monte Carlo Log File Simulation with Experimental Feedback from Film Dosimetry.

A model based on a specific phantom, called QuAArC, has been designed for the evaluation of planning and verification systems of complex radiotherapy treatments, such as volumetric modulated arc therapy (VMAT). This model uses the high accuracy provided by the Monte Carlo (MC) simulation of log files and allows the experimental feedback from the high spatial resolution of films hosted in QuAArC. This cylindrical phantom was specifically designed to host films rolled at different radial distances able to take into account the entrance fluence and the 3D dose distribution. Ionization chamber measurements are also included in the feedback process for absolute dose considerations. In this way, automated MC simulation of treatment log files is implemented to calculate the actual delivery geometries, while the monitor units are experimentally adjusted to reconstruct the dose-volume histogram (DVH) on the patient CT. Prostate and head and neck clinical cases, previously planned with Monaco and Pinnacle treatment planning systems and verified with two different commercial systems (Delta4 and COMPASS), were selected in order to test operational feasibility of the proposed model. The proper operation of the feedback procedure was proved through the achieved high agreement between reconstructed dose distributions and the film measure- ments (global gamma passing rates > 90% for the 2%/2 mm criteria). The necessary discre- tization level of the log file for dose calculation and the potential mismatching between calculated control points and detection grid in the verification process were discussed. Besides the effect of dose calculation accuracy of the analytic algorithm implemented in treatment planning systems for a dynamic technique, it was discussed the importance of the detection density level and its location in VMAT specific phantom to obtain a more reliable DVH in the patient CT. The proposed model also showed enough robustness and efficiency to be considered as a pre-treatment VMAT verification system.Ministerio de Ciencia y Tecnología SAF2011- 27116; IPT-2011-1480-900000