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EPID – a useful interfraction QC tool

Klimas Aleksandra, Grządziel Aleksandra, Plaza Dominika, Bekman Barbara, Woźniak Bożena, Dolla Łukasz, Osewski Wojciech, Paściak Paweł, Wendykier Jacek, Ślosarek Krzysztof

Polish Journal of Medical Physics and Engineering

2019

Vol. 25, Iss. 4

221--228

Biomedical accelerators used in radiotherapy are equipped with detector arrays which are commonly used to obtain the image of patient position during the treatment session. These devices use both kilovolt and megavolt x-ray beams. The advantage of EPID (Electronic Portal Imaging Device) megavolt panels is the correlation of the measured signal with the calibrated dose. The EPID gives a possibility to verify delivered dose. The aim of the study is to answer the question whether EPID can be useful as a tool for interfraction QC (quality control) of dose and geometry repeatability. The EPID system has been calibrated according to the manufacturer’s recommendations to obtain a signal and dose values correlation. Initially, the uncertainty of the EPID matrix measurement was estimated. According to that, the detecting sensitivity of two parameters was checked: discrepancies between the planned and measured dose and field geometry variance. Moreover, the linearity of measured signal-dose function was evaluated. In the second part of the work, an analysis of several dose distributions was performed. In this study, the analysis of clinical cases was limited to stereotactic dynamic radiotherapy. Fluence maps were obtained as a result of the dose distribution measurements with the EPID during treatment sessions. The compatibility of fluence maps was analyzed using the gamma index. The fluence map acquired during the first fraction was the reference one. The obtained results show that EPID system can be used for interfraction control of dose and geometry repeatability.

Gantry angle classification with a fluence map in intensity-modulated radiotherapy for prostate cases using machine learning

Miura H., Ozawa S., Enosaki T., Hayata M., Yamada K., Nagata Y.

Polish Journal of Medical Physics and Engineering

2018

Vol. 24, Iss. 4

165--169

We investigated the gantry-angle classifier performance with a fluence map using three machine-learning algorithms, and compared it with human performance. Eighty prostate cases were investigated using a seven-field-intensity modulated radiotherapy treatment (IMRT) plan with beam angles of 0°, 50°, 100°, 155°, 205°, 260°, and 310°. The k- nearest neighbor (k-NN), logistic regression (LR), and support vector machine (SVM) algorithms were used. In the observer test, three radiotherapists assessed the gantry angle classification in a blind manner. The precision and recall rates were calculated for the machine learning and observer test. The average precision rate of the k-NN and LR algorithms were 94.8% and 97.9%, respectively. The average recall rate of the k-NN and LR algorithms were 94.3% and 97.9%, respectively. The SVM had 100% precision and recall rates. The gantry angles of 0°, 155°, and 205° had an accuracy of 100% in all algorithms. In the observer test, average precision and recall rates were 82.6% and 82.6%, respectively. All observers could easily classify the gantry angles of 0°, 155°, and 205° with a high degree of accuracy. Misclassifications occurred in gantry angles of 50°, 100°, 260°, and 310°. Machine learning could better classify gantry angles for prostate IMRT than human beings. In particular, the SVM algorithm had a perfect classification of 100%