Engineering topics

Scheuermann, R

Publications and source records attributed to Scheuermann, R.

3 records

DOE OSTI2016

MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

Purpose: To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. Methods: A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results: In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predict gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. Conclusion: We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-C-BRD-03: Closing the Loop On Virtual IMRT QA

Purpose: To develop an algorithm that predicts a priori IMRT QA passing rates. Methods: 416 IMRT plans from all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam linacs (Varian Medical Systems, Palo Alto, CA). The 3%/3mm and 2%/2mm local distance to agreement (DTA) were recorded during clinical operations using a commercial 2D diode array (MapCHECK 2, Sun Nuclear, Melbourne, FL). Each plan was characterized by 37 metrics that describe different failure modes between the calculated and measured dose. Machine-learning algorithms (MLAs) were trained to learn the relation between the plan characteristics and each passing rate. Minimization of the cross validated error, together with maximum a posteriori estimation (MAP), were used to choose the model parameters. Results: 3%/3mm local DTA can be predicted with an error smaller than 3% for 98% of the plans. For the remaining 2% of plans, the residual error was within 5%. For 2%/2mm local DTA passing rates, 96% percent of the plans were successfully predicted with an error smaller than 5%. All high-risk plans that failed the 2%/2mm local criteria were correctly identified by the algorithm. The most important metric to describe the passing rates was determined to be the MU per Gray (modulation factor). Conclusions: Logs files and independent dose calculations have been suggested as possible substitutes for measurement based IMRT QA. However, none of these methods answer the fundamental question of whether a plan can be delivered with a clinically acceptable error given the limitations of the linacs and the treatment planning system. Predicting the IMRT QA passing rates a priori closes that loop. For additional robustness, virtual IMRT QA can be combined with Linac QA and log file analysis to confirm appropriate delivery.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-T-153: Establish a Comprehensive Patient-Specific Plan QA Database for Instituitional Quality Control Program

Purpose: Since the publication of TG-119, several new treatment techniques requiring new QA devices have been implemented in the clinic. To monitor and analyze our institutional QA performance, we have created a comprehensive QA database using Research Electronic Data Capture (REDCap). The database will also assist us in creating IMRT QA Analysis practice guidelines. Methods: Since Oct. 2013, 336 patient plan QA results were entered into the database. Plan parameters such as plan ID, treatment site, technique, energy, optimization constraints, modulation factor, leaf speed, leaf opening.etc were automatically extracted from the Varian Eclipse database to allow us to refine our evaluation and analysis method. Specific QA device, LINAC-related information, and measurement and analysis results were manually entered by the QA team. IMRT plans were measured using MapCHECK2 while RapidArc plans were measured using ArcCHECK. Distance-To-Agreement 3%/3mm without global maximum normalization was used. Results: The data indicates that different treatment techniques might benefit from a different site-specific action level(AL) depending on the complexity of the plan and optimization parameter used (e.g., breast IMRT QA= 97.8% and pelvis IMRT QA=93.1%). Different QA devices may also benefit from a different AL (MapCHECK2 = 94.1% while ArcCHECK = 83.0%). The relationship between the parameters and passing rate suggests that the complexity of each plan, characterized by leaf travel, leaf opening and modulation factor, affect the passing rate significantly. The database is reviewed regularly, and any abnormal point of the QA result or a trend of lower QA passing rate on a specific LINAC is further investigated. Conclusion: Establishing a comprehensive QA database provides an overview of the quality assurance program. It not only helps in answering the question “what is a reasonable and achievable standard for each institution”, but also saves time in monitoring and investigating the daily machine and QA performance.

07 ISOTOPE AND RADIATION SOURCES↗