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

Abstract

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 \(\gamma\)-index pass rates of a 4 cm \(\times\) 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.