A synthesized gamma distribution‐based patient‐specific VMAT QA using a generative adversarial network

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 (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated. Results The root mean squared errors between mGPR and pGPR 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.

Virtual pretreatment patient‐specific quality assurance of volumetric modulated arc therapy using deep learning

“…Their model classified the IMRT fields as either pass or fail with an accuracy of 95.79%, and the mean absolute error of GPR was 2.52 for 2%/2 mm gamma criteria (absolute dose mode and 10% dose threshold). A very recent work by Matsuura et al 45 . also reported a method to predict the synthesized gamma distribution of VMAT.…”

“… 26 presented a virtual PSQA methodology for IMRT using UNet++, trained to predict three outputs (gamma pass rates, dose differences, and classification results) to determine whether the QA failed or passed. A very recent work by Matsuura et al also reported a method to predict the synthesized gamma distribution of VMAT using generative adversarial networks (GAN) 45 …”

“…[41][42][43][44] Recently, deep learning methods were also explored for automatic PSQA. [24][25][26][27][28][29][30]45 Hao et al 29 used an optimized CNN model to predict the GPR of IMRT fields. Huang et al 26 presented a virtual PSQA methodology for IMRT using UNet++, trained to predict three outputs (gamma pass rates, dose differences, and classification results) to determine whether the QA failed or passed.…”

Unbiased evaluation of predicted gamma passing rate by an event‐mixing technique

“…Different characteristics in the intensity modulation may affect the difficulty in predicting GPR values and resulting AS, AF, and $${\sigma _p}$$. It is also noted that our evaluation scheme needs to be examined with the other QA devices such as Delta4 9 or portal dosimetry 20 . These devices and ArcCHECK measure different distributions with different aspects.…”

“…Many authors have been developing methods to predict the GPR from a model developed by learning characteristics of the treatment plan, such as the complexity metric, [3][4][5][6][7] dose uncertainty potential (DUP), 8,9 machine learning, [10][11][12][13][14][15][16] and deep learning techniques. [17][18][19][20][21] The achievement of the predicting model is evaluated using such as a standard deviation (SD) of the difference between the measured GPR [m] and the predicted GPR [p], [8][9][10][11][12]18 and Pearson's correlation coefficient (CC) of the (m, p) pairs. 3,5,[10][11][12]17,18,21 However, these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array.…”