祝贺职少华同学的论文被国际权威杂志IEEE TMI接受 - 个人简介 - 牟 轩沁
职少华同学为第一作者的论文“CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement”被本领域的国际权威杂志IEEE Transactions on Medical Imaging杂志接受发表，在此表示祝贺！
Title: CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement
Authors: Shaohua Zhi, Marc Kachelrieß, Fei Pan, and Xuanqin Mou
Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.