A synthesized gamma distribution‐based patient‐specific VMAT QA using a generative adversarial network. Issue 4 (15th January 2023)

• Record Type: Journal Article
• Authors: Matsuura, Takaaki; Kawahara, Daisuke; Saito, Akito; Yamada, Kiyoshi; Ozawa, Shuichi; Nagata, Yasushi
• Is Part Of: Medical physics. Volume 50:Issue 4(2023)
• Page Start: 2488
• Page End: 2498
• Publication Date: 2023-01-15
• Subjects: deep learning -- gamma distribution -- gamma passing rate -- GAN; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16210
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Artificial intelligence (AI)‐based gamma passing rate (GPR) prediction has been proposed as a time‐efficient virtual patient‐specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy. Purpose: The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN). Methods: The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2‐mm, 2%/1‐mm, 1%/1‐mm, and 1%/0.5‐mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR ( m GPR) and predicted GPR ( p GPR) values calculated from the synthesized gamma distributions was evaluated. Results: The root mean squared errors between m GPR and p GPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2‐mm, 2%/1‐mm, 1%/1‐mm, and 1%/0.5‐mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2‐mm, 2%/1‐mm, 1%/1‐mm, and 1%/0.5‐mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5‐mm and 3%/2‐mm tolerances, which were 82.7% and 99.6%, respectively. Conclusions: We developed a novel system using a GAN to generate a synthesized gamma distribution‐based patient‐specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.