A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files

Article type: Research Article

Authors: Huang, Ying^{a; b; c; 1} | Cai, Ruxin^{c; 1} | Pi, Yifei^d | Ma, Kui^e | Kong, Qing^a | Zhuo, Weihai^{b; *} | Kong, Yan^{f; *}

Affiliations: [a] Institute of Modern Physics, Fudan University, Shanghai, China | [b] Institute of Radiation Medicine, Fudan University, Shanghai, China | [c] Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China | [d] Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Henan, China | [e] Varian Medical Systems, Beijing, China | [f] Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Jiangsu, China

Correspondence: [*] Corresponding authors: Weihai Zhuo, Full postal address: Institute of Radiation Medicine, Fudan University, 200032, Shanghai, China; E-mail: whzhuo@fudan.edu.cn and Yan Kong, Full postal address: Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 214122, Wuxi, Jiangsu, China; E-mail: ky_qingyan@163.com.

Note: [1] These authors have contributed equally to this work and share first authorship.

Abstract: OBJECTIVE: This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery. METHODS: A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model. RESULTS: Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P < 0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria. CONCLUSIONS: In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation.

Keywords: Three-dimensional gamma prediction, log files, patient-specific quality assurance

DOI: 10.3233/XST-230412

Journal: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1199-1208, 2024