Abstract

Info

Title:: Generation of synthetic CT image from head cone beam CT image using DenseCUT

Author(s):: WU Xinhong1; WANG Jiangtao1; TANG Wei2; ZUO Yang1; 3; LU Hsiao-Ming2; ZHU Lei1; YANG Yidong1; 3; 1. Department of Engineering and Applied Physics, University of Science and Technology of China, Heifei 230026, China 2. Hefei Ion Medical Center, Hefei 230088, China 3. Department of Radiotherapy, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China

Keywords:: Keywords: cone beam CT synthetic CT dense contrastive unpaired translation network

Abstract:: Abstract: An unsupervised deep learning network is presented for generating synthetic CT (sCT) images from cone beam CT (CBCT) images of the head, and it is compared with cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT). After collecting the planning CT (pCT) images and CBCT images of 56 brain tumor patients (49 for training and 7 for testing), the sCT images are generated from CBCT images using CycleGAN, CUT, and the proposed dense contrastive unpaired translation (DenseCUT), separately. DenseCUT has two novelties, namely combining the CUT network with the dense block network, and adding structural similarity to the loss function. Compared with pCT-CBCT, pCT-sCT (DenseCUT) reduces the average absolute error of the HU from 34.38 HU to 17.75 HU, increases the peak signal-to-noise ratio from 26.19 dB to 29.83 dB, and elevates the structural similarity from 0.78 to 0.87. The proposed method can generate high-quality sCT images from CBCT images without altering the anatomical structures, while reducing image artifacts, which makes it possible for CBCT to be applied to dose calculation and adaptive radiotherapy planning.