Validation of an in vivo transit dosimetry algorithm using Monte Carlo simulations and ionization chamber measurements

Purpose Transit dosimetry is a safety tool based on the transit images acquired during treatment. Forward-projection transit dosimetry software, as PerFRACTION, compares the transit images acquired with an expected image calculated from the DICOM plan, the CT, and the structure set. This work aims to validate PerFRACTION expected transit dose using PRIMO Monte Carlo simulations and ionization chamber measurements, and propose a methodology based on MPPG5a report. Methods The validation process was divided into three groups of tests according to MPPG5a: basic dose validation, IMRT dose validation, and heterogeneity correction validation. For the basic dose validation, the fields used were the nine fields needed to calibrate PerFRACTION and three jaws-defined. For the IMRT dose validation, seven sweeping gaps fields, the MLC transmission and 29 IMRT fields from 10 breast treatment plans were measured. For the heterogeneity validation, the transit dose of these fields was studied using three phantoms: 10 , 30 , and a 3 cm cork slab placed between 10 cm of solid water. The PerFRACTION expected doses were compared with PRIMO Monte Carlo simulation results and ionization chamber measurements. Results Using the 10 cm solid water phantom, for the basic validation fields, the root mean square (RMS) of the difference between PerFRACTION and PRIMO simulations was 0.6%. In the IMRT fields, the RMS of the difference was 1.2%. When comparing respect ionization chamber measurements, the RMS of the difference was 1.0% both for the basic and the IMRT validation. The average passing rate with a ¿(2%/2 mm, TH = 20%) criterion between PRIMO dose distribution and PerFRACTION expected dose was 96.0% ± 5.8%. Conclusion We validated PerFRACTION calculated transit dose with PRIMO Monte Carlo and ionization chamber measurements adapting the methodology of the MMPG5a report. The methodology presented can be applied to validate other forward-projection transit dosimetry software.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Postprint (published version

Gamma passing rates of daily EPID transit images correlate to PTV coverage for breast cancer IMRT treatment plans

The use of the transit image obtained with the electronic portal-imaging device (EPID) is becoming an extended method to perform in-vivodosimetry. The transit images acquired during each fraction can be comparedwith a predicted image, if available, or with a baseline image, usually theobtained in the first fraction.This work aims to study the dosimetric impact of thefailing fractions and to evaluate the appropriateness of using a baseline imagein breast plans.Material and methods:Twenty breast patients treated in a Halcyon were ret-rospectively selected. For each patient and fraction, the treatment plan wascalculated over the daily CBCT image.For each fraction,the differences respectto the treatment plan values of OARs and PTV dosimetric parameters wereanalyzed:¿Dmean,¿D95%,¿D98%,¿D2%,¿V36Gy,¿V38.5Gy, and¿V43.5Gy.Daily fractions were ranked according to the differences found in the dosimet-ric parameters between the treatment plan and the daily CBCT to establish thebest fraction.The daily transit images acquired in every fraction were comparedto the first fraction using the global gamma index with the Portal Dosime-try tool. The comparison was repeated using the best fraction image as abaseline.We assessed the correlation of the dosimetric differences obtained from theCBCT images-based treatment plans with the gamma index passing ratesobtained using first fraction and best fraction as baseline.Results:Average values of -11.6% [-21.4%, -3.3%] and -3.2% [-1.0%, -10.3%]for the¿PTVD98% and¿PTVD95% per every 10% decrease in the passingrate were found, respectively.When using the best fraction as baseline patients were detected with failingfractions that were not detected with the first fraction as baseline.Conclusion:The gamma passing rates of daily transit images correlate withthe coverage loss parameters in breast IMRT plans. Using first fraction imageas baseline can lead to the non-detectability of failing fractionsPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

Assessment of the Monitor Unit Objective tool for VMAT in the Eclipse treatment planning system

AimThis work aims to achieve the highest possible monitor units (MU) reduction using the MU Objective tool included in the Eclipse treatment planning system, while preserving the plan quality.BackgroundThe treatment planning system Eclipse (Varian Medical Systems, Palo Alto, CA) includes a control mechanism for the number of monitor units of volumetric modulated arc therapy (VMAT) plans, named the MU Objective tool.Material and methodsForty prostate plans, 20 gynecological plans and 20 head and neck plans designed with VMAT were retrospectively studied. Each plan (base plan) was optimized without using the MU Objective tool, and it was re-optimized with different values of the Maximum MU (MaxMU) parameter of the MU Objective tool. MU differences were analyzed with a paired samples t-test and changes in plan quality were assessed with a set of parameters for OARs and PTVs.ResultsThe average relative MU difference [[mml:math altimg="si2.gif"]][[mml:mrow]][[mml:mo stretchy="false"]]([[/mml:mo]][[mml:mover accent="true"]][[mml:mrow]][[mml:mstyle mathvariant="normal"]][[mml:mi]]Δ[[/mml:mi]][[/mml:mstyle]][[mml:mi]]M[[/mml:mi]][[mml:mi]]U[[/mml:mi]][[/mml:mrow]][[mml:mo stretchy="true"]]¯[[/mml:mo]][[/mml:mover]][[mml:mo stretchy="false"]])[[/mml:mo]][[/mml:mrow]][[/mml:math]] considering all treatment sites, was the highest when MaxMU[[ce:hsp sp="0.25"/]]=[[ce:hsp sp="0.25"/]]400 (−4.2%, p[[ce:hsp sp="0.25"/]

PRIMO Monte Carlo software benchmarked against a reference dosimetry dataset for 6 MV photon beams from Varian linacs

Abstract Background The software PRIMO for the Monte Carlo simulation of radiotherapy linacs could potentially act as a independent calculation system to verify the calculations of treatment planning systems. We investigated the suitability of the PRIMO default beam parameters to produce accurate dosimetric results for 6 MV photon beams from Varian Clinac 2100 linacs and 6 MV flattening–filter–free photon beams from Varian TrueBeam linacs. Methods Simulation results with the DPM algorithm were benchmarked against a published reference dosimetry dataset based on point measurements of 25 dosimetric parameters on a large series of linacs. Studied parameters (for several field sizes and depths) were: PDD, off–axis ratios, and output factors for open fields and IMRT/SBRT–style fields. For the latter, the output factors were also determined with radiochromic film and with a small–sized ionization chamber. Benchmark data, PRIMO simulation results and our experimental results were compared. Results PDD, off–axis ratios, and open–field output factors obtained from the simulations with the PRIMO default beam parameters agreed with the benchmark data within 2.4% for Clinac 2100, and within 1.3% for TrueBeam. Higher differences were found for IMRT/SBRT–style output factors: up to 2.8% for Clinac 2100, and up to 3.3% for TrueBeam. Experimental output factors agreed with benchmark data within 1.0% (ionization chamber) and within 1.9% (radiochromic film). Conclusions PRIMO default initial beam parameters for 6 MV photon beams from Varian Clinac 2100 linacs and 6 MV FFF photon beams from Varian TrueBeam linacs allowed agreement within 3.3% with a dosimetry database based on measurements of a high number of linacs. This finding represents a first step in the validation of PRIMO for the independent verification of radiotherapy plans

Simulation Tools

Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection.Peer reviewe