MO-FG-202-07: Real-Time EPID-Based Detection Metric For VMAT Delivery Errors.
Publisher DOI
PubMed ID
28048000
Abstract
PURPOSE
To create and test an accurate EPID-frame-based VMAT QA metric to detect gross dose errors in real-time and to provide information about the source of error.
METHODS
A Swiss cheese model was created for an EPID-based real-time QA process. The system compares a treatmentplan- based reference set of EPID images with images acquired over each 2° gantry angle interval. The metric utilizes a sequence of independent consecutively executed error detection METHODS: a masking technique that verifies infield radiation delivery and ensures no out-of-field radiation; output normalization checks at two different stages; global image alignment to quantify rotation, scaling and translation; standard gamma evaluation (3%, 3 mm) and pixel intensity deviation checks including and excluding high dose gradient regions. Tolerances for each test were determined. For algorithm testing, twelve different types of errors were selected to modify the original plan. Corresponding predictions for each test case were generated, which included measurement-based noise. Each test case was run multiple times (with different noise per run) to assess the ability to detect introduced errors.
RESULTS
Averaged over five test runs, 99.1% of all plan variations that resulted in patient dose errors were detected within 2° and 100% within 4° (∼1% of patient dose delivery). Including cases that led to slightly modified but clinically equivalent plans, 91.5% were detected by the system within 2°. Based on the type of method that detected the error, determination of error sources was achieved.
CONCLUSION
An EPID-based during-treatment error detection system for VMAT deliveries was successfully designed and tested. The system utilizes a sequence of methods to identify and prevent gross treatment delivery errors. The system was inspected for robustness with realistic noise variations, demonstrating that it has the potential to detect a large majority of errors in real-time and indicate the error source. J. V. Siebers receives funding support from Varian Medical Systems.
To create and test an accurate EPID-frame-based VMAT QA metric to detect gross dose errors in real-time and to provide information about the source of error.
METHODS
A Swiss cheese model was created for an EPID-based real-time QA process. The system compares a treatmentplan- based reference set of EPID images with images acquired over each 2° gantry angle interval. The metric utilizes a sequence of independent consecutively executed error detection METHODS: a masking technique that verifies infield radiation delivery and ensures no out-of-field radiation; output normalization checks at two different stages; global image alignment to quantify rotation, scaling and translation; standard gamma evaluation (3%, 3 mm) and pixel intensity deviation checks including and excluding high dose gradient regions. Tolerances for each test were determined. For algorithm testing, twelve different types of errors were selected to modify the original plan. Corresponding predictions for each test case were generated, which included measurement-based noise. Each test case was run multiple times (with different noise per run) to assess the ability to detect introduced errors.
RESULTS
Averaged over five test runs, 99.1% of all plan variations that resulted in patient dose errors were detected within 2° and 100% within 4° (∼1% of patient dose delivery). Including cases that led to slightly modified but clinically equivalent plans, 91.5% were detected by the system within 2°. Based on the type of method that detected the error, determination of error sources was achieved.
CONCLUSION
An EPID-based during-treatment error detection system for VMAT deliveries was successfully designed and tested. The system utilizes a sequence of methods to identify and prevent gross treatment delivery errors. The system was inspected for robustness with realistic noise variations, demonstrating that it has the potential to detect a large majority of errors in real-time and indicate the error source. J. V. Siebers receives funding support from Varian Medical Systems.
Date Issued
2016-06
Publication Type
Conference Item
Subject(s)
Subjects
Dosimetry
•
Error correction
•
Gamma rays
•
Image guided radiation therapy
•
Medical imaging
•
Radiation treatment
•
Real time information delivery
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Science funding
•
Testing procedures
Language(s)
en
Author(s)
Passarge, M | |
Stampanoni, M F M | |
Siebers, J V |
Additional Credits
Journal
Medical physics
Publisher
American Association of Physicists in Medicine AAPM
ISSN
0094-2405
Title of Event
Access(Rights)
metadata.only