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Title: SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System

Abstract

Purpose: The current status quo of QA data management consists of a mixture of paper-based forms and spreadsheets for recording the results of daily, monthly, and yearly QA tests for both CT scanners and LINACs. Unfortunately, such systems suffer from a host of problems as, (1) records can be easily lost or destroyed, (2) data is difficult to access — one must physically hunt down records, (3) poor or no means of historical data analysis, and (4) no remote monitoring of machine performance off-site. To address these issues, a cloud based QA data management system was developed and implemented. Methods: A responsive tablet interface that optimizes clinic workflow with an easy-to-navigate interface accessible from any web browser was implemented in HTML/javascript/CSS to allow user mobility when entering QA data. Automated image QA was performed using a phantom QA kit developed in Python that is applicable to any phantom and is currently being used with the Gammex ACR, Las Vegas, Leeds, and Catphan phantoms for performing automated CT, MV, kV, and CBCT QAs, respectively. A Python based resource management system was used to distribute and manage intensive CPU tasks such as QA phantom image analysis or LaTeX-to-PDF QA report generation tomore » independent process threads or different servers such that website performance is not affected. Results: To date the cloud QA system has performed approximately 185 QA procedures. Approximately 200 QA parameters are being actively tracked by the system on a monthly basis. Electronic access to historical QA parameter information was successful in proactively identifying a Linac CBCT scanner’s performance degradation. Conclusion: A fully comprehensive cloud based QA data management system was successfully implemented for the first time. Potential machine performance issues were proactively identified that would have been otherwise missed by a paper or spreadsheet based QA system.« less

Authors:
; ; ;  [1]
  1. The University of Chicago, Chicago, IL (United States)
Publication Date:
OSTI Identifier:
22545146
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; COMPUTERIZED TOMOGRAPHY; DATA ANALYSIS; IMAGE PROCESSING; IMAGES; LATEX; LINEAR ACCELERATORS; PERFORMANCE; PHANTOMS

Citation Formats

Wiersma, R, Grelewicz, Z, Belcher, A, and Liu, X. SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System. United States: N. p., 2015. Web. doi:10.1118/1.4924372.
Wiersma, R, Grelewicz, Z, Belcher, A, & Liu, X. SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System. United States. doi:10.1118/1.4924372.
Wiersma, R, Grelewicz, Z, Belcher, A, and Liu, X. Mon . "SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System". United States. doi:10.1118/1.4924372.
@article{osti_22545146,
title = {SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System},
author = {Wiersma, R and Grelewicz, Z and Belcher, A and Liu, X},
abstractNote = {Purpose: The current status quo of QA data management consists of a mixture of paper-based forms and spreadsheets for recording the results of daily, monthly, and yearly QA tests for both CT scanners and LINACs. Unfortunately, such systems suffer from a host of problems as, (1) records can be easily lost or destroyed, (2) data is difficult to access — one must physically hunt down records, (3) poor or no means of historical data analysis, and (4) no remote monitoring of machine performance off-site. To address these issues, a cloud based QA data management system was developed and implemented. Methods: A responsive tablet interface that optimizes clinic workflow with an easy-to-navigate interface accessible from any web browser was implemented in HTML/javascript/CSS to allow user mobility when entering QA data. Automated image QA was performed using a phantom QA kit developed in Python that is applicable to any phantom and is currently being used with the Gammex ACR, Las Vegas, Leeds, and Catphan phantoms for performing automated CT, MV, kV, and CBCT QAs, respectively. A Python based resource management system was used to distribute and manage intensive CPU tasks such as QA phantom image analysis or LaTeX-to-PDF QA report generation to independent process threads or different servers such that website performance is not affected. Results: To date the cloud QA system has performed approximately 185 QA procedures. Approximately 200 QA parameters are being actively tracked by the system on a monthly basis. Electronic access to historical QA parameter information was successful in proactively identifying a Linac CBCT scanner’s performance degradation. Conclusion: A fully comprehensive cloud based QA data management system was successfully implemented for the first time. Potential machine performance issues were proactively identified that would have been otherwise missed by a paper or spreadsheet based QA system.},
doi = {10.1118/1.4924372},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: Patient-specific QA for VMAT is incapable of providing full 3D dosimetric information and is labor intensive in the case of severe heterogeneities or small-aperture beams. A cloud-based Monte Carlo dose reconstruction method described here can perform the evaluation in entire 3D space and rapidly reveal the source of discrepancies between measured and planned dose. Methods: This QA technique consists of two integral parts: measurement using a phantom containing array of dosimeters, and a cloud-based voxel Monte Carlo algorithm (cVMC). After a VMAT plan was approved by a physician, a dose verification plan was created and delivered to the phantommore » using our Varian Trilogy or TrueBeam system. Actual delivery parameters (i.e., dose fraction, gantry angle, and MLC at control points) were extracted from Dynalog or trajectory files. Based on the delivery parameters, the 3D dose distribution in the phantom containing detector were recomputed using Eclipse dose calculation algorithms (AAA and AXB) and cVMC. Comparison and Gamma analysis is then conducted to evaluate the agreement between measured, recomputed, and planned dose distributions. To test the robustness of this method, we examined several representative VMAT treatments. Results: (1) The accuracy of cVMC dose calculation was validated via comparative studies. For cases that succeeded the patient specific QAs using commercial dosimetry systems such as Delta- 4, MAPCheck, and PTW Seven29 array, agreement between cVMC-recomputed, Eclipse-planned and measured doses was obtained with >90% of the points satisfying the 3%-and-3mm gamma index criteria. (2) The cVMC method incorporating Dynalog files was effective to reveal the root causes of the dosimetric discrepancies between Eclipse-planned and measured doses and provide a basis for solutions. Conclusion: The proposed method offers a highly robust and streamlined patient specific QA tool and provides a feasible solution for the rapidly increasing use of VMAT treatments in the clinic.« less
  • Purpose: Raven QA (JPLC, MD) is a unified and comprehensive quality assurance system for QA of TG-142, which use a phosphor screen, a mirror system and a camera. It is to test if this device can be used for IMRT QA dosimetry. Methods: A lung IMRT case is used deliver dose to Raven QA. Accuracy of dose distribution of 5cm slab phantom using Eclipse planning system (Varian) has been confirmed both from a Monte Carlo Simulation and from a MapCheck (SunNuclear) measurement. Geometric distortion and variation of spatial dose response are corrected after background subtraction. A pin-hole grid plate ismore » designed and used to determine the light scatter in the Raven QA box and the spatial dose response. Optic scatter model was not applied in this preliminary study. Dose is normalized to the response of the 10×10 field and the TMR of 5cm depth was considered. Results: Time to setup the device for IMRT QA takes less than 5 minutes as other commercially available devices. It shows excellent dose linearity and dose rate independent, less than 1 %. Background signal, however, changes for different field sizes. It is believed to be due to inaccurate correction of optic scatter. Absolute gamma (5%, 5mm) passing rate was higher than 95%. Conclusion: This study proves that the Raven QA can be used for a patient specific IMRT verification. Part of this study is supported by the Maryland Industrial Partnership Grant.« less
  • Purpose: VMAT involves two main sources of uncertainty: one related to the dose calculation accuracy, and the other linked to the continuous delivery of a discrete calculation. The purpose of this work is to present QuAArC, an alternative VMAT QA system to control and potentially reduce these uncertainties. Methods: An automated MC simulation of log files, recorded during VMAT treatment plans delivery, was implemented in order to simulate the actual treatment parameters. The linac head models and the phase-space data of each Control Point (CP) were simulated using the EGSnrc/BEAMnrc MC code, and the corresponding dose calculation was carried outmore » by means of BEAMDOSE, a DOSXYZnrc code modification. A cylindrical phantom was specifically designed to host films rolled up at different radial distances from the isocenter, for a 3D and continuous dosimetric verification. It also allows axial and/or coronal films and point measurements with several types of ion chambers at different locations. Specific software was developed in MATLAB in order to process and evaluate the dosimetric measurements, which incorporates the analysis of dose distributions, profiles, dose difference maps, and 2D/3D gamma index. It is also possible to obtain the experimental DVH reconstructed on the patient CT, by an optimization method to find the individual contribution corresponding to each CP on the film, taking into account the total measured dose, and the corresponding CP dose calculated by MC. Results: The QuAArC system showed high reproducibility of measurements, and consistency with the results obtained with the commercial system implemented in the verification of the evaluated treatment plans. Conclusion: A VMAT QA system based on MC simulation and high resolution dosimetry with film has been developed for treatment verification. It shows to be useful for the study of the real VMAT capabilities, and also for linac commissioning and evaluation of other verification devices.« less
  • Purpose: To evaluate the accuracy of volumetric modulated arc therapy (VMAT) treatment delivery dose clouds by comparing linac log data to doses measured using an ionization chamber and film. Methods: A commercial IMRT quality assurance (QA) process utilizing a DICOM-RT framework was tested for clinical practice using 30 prostate and 30 head and neck VMAT plans. Delivered 3D VMAT dose distributions were independently checked using a PinPoint ionization chamber and radiographic film in a solid water phantom. DICOM RT coordinates were used to extract the corresponding point and planar doses from 3D log file dose distributions. Point doses were evaluatedmore » by computing the percent error between log file and chamber measured values. A planar dose evaluation was performed for each plan using a 2D gamma analysis with 3% global dose difference and 3 mm isodose point distance criteria. The same analysis was performed to compare treatment planning system (TPS) doses to measured values to establish a baseline assessment of agreement. Results: The mean percent error between log file and ionization chamber dose was 1.0%±2.1% for prostate VMAT plans and −0.2%±1.4% for head and neck plans. The corresponding TPS calculated and measured ionization chamber values agree within 1.7%±1.6%. The average 2D gamma passing rates for the log file comparison to film are 98.8%±1.0% and 96.2%±4.2% for the prostate and head and neck plans, respectively. The corresponding passing rates for the TPS comparison to film are 99.4%±0.5% and 93.9%±5.1%. Overall, the point dose and film data indicate that log file determined doses are in excellent agreement with measured values. Conclusion: Clinical VMAT QA practice using LINAC treatment log files is a fast and reliable method for patient-specific plan evaluation.« less
  • Purpose: To quantify the dosimetric impact on patient’s specific treatment plans due to set up uncertainties during LINAC commission and annual QA and to determine the maximum set up uncertainty allowance range. Methods: A 60×60×60 cm{sup 3} solid water cube was created in Varian Eclipse TPS. Beam data profiles (crossline and diagonal) and PDDs for field sizes ranging from 2×2 cm{sup 2} to 40×40 cm{sup 2} were simulated. Three main uncertainty scenarios were purposely introduced for gantry position tilts (0–5°), source axis distance changes (100–105 cm), and iso-center position shifts (0–5 mm) during the simulation. A gamma analysis was usedmore » to compare the correct simulated profiles with the profiles for each scenario. Two static IMRT treatment plans (H&N and GYN) with tumors at 5 cm and 15 cm depths were compared using similar set up uncertainties. Results: A gamma analysis using ±3%/±3mm with 90% passing rate criteria is included to show the passing rate for each scenario. Crossline and diagonal profiles showed a gamma passing rating of ≥ 90% at depth ≤10 cm for these scenarios: gantry tilted from 0–5°, SAD changed from 100–105 cm, and iso-center shifted ≤ 4 mm. From 10 to 20 cm depths, all three scenarios failed with gamma passing ≤ 90% excepted for diagonal profiles at Gantry =2°, SAD =1 cm, and iso-center =1 mm off center. Diagonal profiles showed a higher gamma passing rating compared to crossline profiles for all three scenarios. PDD differences also increased as depth increased. For patient’s specific treatment plans, maximum uncertainties allowed to obtain a ≥90% gamma passing rating are: gantry tilts ±1 degree, SAD shifts ±2 cm, and iso-center moves ±3 mm. Conclusion: This study validated AAPM TG 142 recommendations on the mechanical and dosimetry uncertainties and provided proofs on maximum acceptance tolerances for LINAC annual QA and commission.« less