Automated Quality Assurance for Image-Guided Radiation Therapy

Abstract

Purpose: The use of image-guided patient positioning requires fast and reliable Quality Assurance (QA) methods to ensure the megavoltage (MV) treatment beam coincides with the integrated kilovoltage (kV) or volumetric cone-beam CT (CBCT) imaging and guidance systems. This study automates the standard QA protocol by using imaging filters to detect relevant QA features in the planar and volumetric X-ray images.

Method and Materials: Current QA protocol is based on visually observing deviations of certain features in acquired kV in-room treatment images such as markers, distances, or HU values from phantom specifications. This is a time-consuming and subjective task because these features are identified by human operators. The method implemented in this study automated an IGRT QA protocol by using specific image processing algorithms that rigorously detected phantom features and performed all measurements involved in a classical QA protocol. The algorithm was tested on four different IGRT QA phantoms.

Results: Image analysis algorithms were able to detect QA features with the same accuracy as the manual approach but significantly faster. The QA test is routinely implemented on all four IGRT-enabled machines available at our institution. All described tests are performed in a single procedure, with acquisition of the images taking ~ 5 minutes, and the automated software analysis taking less than 1 minute.

Conclusion: The automated image analysis based procedure may be used as a daily QA procedure because it is completely automated and uses a single phantom setup. It adds convenience tools such as reporting, database storage and trends analysis