Cone-beam computed tomography noise reduction method based on U-Net with convolutional block attention module in proton therapy

作     者：Xing-Yue Ruan Xiu-Fang Li Meng-Ya Guo Mei Chen Ming Lv Rui Li Zhi-Ling Chen Xing-Yue Ruan;Xiu-Fang Li;Meng-Ya Guo;Mei Chen;Ming Lv;Rui Li;Zhi-Ling Chen

作者机构：Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai201800China University of Chinese Academy of SciencesBeijing100049China Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai201210China Department of Radiation OncologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghai200025China Shanghai Key Laboratory of Proton therapyShanghai201801China 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2024年第35卷第7期

页      面：89-100页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 100207[医学-影像医学与核医学] 1006[医学-中西医结合] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 080203[工学-机械设计及理论] 1010[医学-医学技术（可授医学、理学学位）] 0802[工学-机械工程] 100106[医学-放射医学] 100602[医学-中西医结合临床] 10[医学] 

基　　金：Digital Medical Equipment Research and Development Project Ministry of Science and Technology China:The development of Synchrotron-based proton therapy system(2016YFC0105400) 

主　　题：Proton therapy Cone-beam CT CBAM-U-Net γ-index 

摘      要：Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with convolutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT, CBCT, and SCT images were compared using four metrics: mean absolute error(MAE), root mean square error(RMSE), peak signal-to-noise ratio(PSNR), and structure similarity index measure(SSIM). The mean values of the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919, respectively,which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model provided relatively explicit information regarding patient tissues. Moreover, it maybe be used in dose calculation and adaptive treatment planning in the future.