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Development of a QA Software Tool for Automatic Verification of Plan Data Transfer and Delivery


G Chen

G Chen*, X Li, Medical College of Wisconsin, Milwaukee, WI

SU-E-T-203 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall

Purpose: Consistency verification between the data from treatment planning system (TPS), record and verification system (R&V), and delivered recorder with visual inspection is time consuming and subject to human error. The purpose of this work is to develop a software tool to automatically perform such verifications.

Methods: Using Microsoft visual C++, a quality assurance (QA) tool was developed to (1) read plan data including gantry/collimator/couch parameters, multi-leaf-collimator leaf positions, and monitor unit (MU) numbers from a TPS (Xio, CMS/Elekta, or RealART, Prowess) via RTP link or DICOM transfer, (2) retrieve imported (prior to delivery) and recorded (after delivery) data from a R&V system (Mosaiq, Elekta) with open database connectivity, calculate MU independently based on the DICOM plan data using a modified Clarkson integration algorithm, and (4) compare all the extracted data to identify possible discrepancy between TPS and R&V, and R&V and delivery.

Results: The tool was tested for 20 patients with 3DCRT and IMRT plans from regular and the online adaptive radiotherapy treatments. It was capable of automatically detecting any inconsistency between the beam data from the TPS and the data stored in the R&V system with an independent MU check and any significant treatment delivery deviation from the plan within a few seconds. With this tool being used prior to and after the delivery as an essential QA step, our clinical online adaptive re-planning process can be speeded up to save a few minutes by eliminating the tedious visual inspection.

Conclusions: A QA software tool has been developed to automatically verify the treatment data consistency from delivery back to plan and to identify discrepancy in MU calculations between the TPS and the secondary MU check. This tool speeds up clinical QA process and eliminating human errors from visual inspection, thus improves safety.

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