Abstract

Info

Title:: Automatic analysis of CT/CBCT image quality based on convolutional neural network

Author(s):: ZHANG Jun1; ZHU Jinhan1; ZHUANG Yongdong2; LIU Xiaowei2; CHEN Lixin1; 1. Sun Yat-sen University Cancer Center/State Key Laboratory of Oncology in South China, Guangzhou 510060, China; 2. School of Physics, Sun Yat-sen University, Guangzhou 510275, China

Keywords:: ; convolutional neural network; computed tomography; cone beam computed tomography; image quality; automatic analysis

Abstract:: Objective To propose an independent program for automatic data analysis which can avoid errors caused by subjective factors of the operator while reducing the quality assurance workload. Methods The computed tomography/cone beam computed tomography (CT/CBCT) images of Catphan500/503/504/600 were classified according to functional modules and studied by convolutional neural networks (CNN). After training, the newly entered CT/CBCT images were automatically identified and sorted by functional modules, and then the related indicators were automatically analyzed, including HU linearity, modulation transfer function and uniformity of those images, aiming to ensure that the image quality met the requirements of clinical application. Results For the CT images of Catphan500 and the CBCT images of Catphan503, the function modules, including CTP401, CTP404 and CTP528, were correctly marked by CNN automatic classification. However, the accuracy of CTP486 didn’t reach 100%, which indicated that some other modules were wrongly classified into CTP486. Meanwhile, the automatic analysis of HU linearity, modulation transfer function and homogeneity of CT was achieved. Conclusion Based on CNN, CT/CBCT images of Catphan can be classified accurately. The next step will be to extend the method to other imaging devices in order to achieve a wider range of automatic image quality assurance.