Usage of Deep Learning-Based Portal Dose Images for Treatment Error Detection with Transit Dosimetry

Abstract

In-vivo dose monitoring with electronic portal imaging devices (EPIDs) in radiotherapy can be performed by comparing a recorded EPID image with a reference expected image, either directly or by first converting it into water-equivalent dose (portal dose). We developed a deep learning model that transforms EPID images into water-equivalent dose images. An analysis framework was created, which compares the portal dose images with expectations. In this work, we test whether the framework, based on the gamma analysis, can detect changes in monitor units. We irradiated an inhomogeneous phantom using a geometric field and acquired EPID images with a planned number of monitor units, as well as with a controlled excess of monitor units. These EPID images were transformed into portal dose images using our deep learning model. Then we evaluated how the gamma passing rate varied with delivered monitor units, investigating several tolerance criteria and thresholds. The errors in monitor units could be well identified by our deep learning-based portal dose analysis framework. The choice of the gamma index threshold influenced the action level. Using a dose threshold of 10% and a tolerance of 3%/3 mm, the gamma passing rate  dropped below  95%  when  the  error  in  monitor   units was larger than 102.4 and 102.3 for global and local gamma index calculations, respectively. For an 80% threshold, the values decreased to 102.2 for global and local calculations. When using a newly developed analysis framework, based on comparison of deep learning-based portal dose images with the gamma index, we confirmed that we can detect errors in monitor units. As a starting point for an alert system, the global gamma index analysis can be used with an 80% threshold and 3%/3 mm or stricter criterion.