CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Imperial College London（伦敦帝国学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose. Because of the reduced number of projection views, traditional reconstruction methods can lead to severe artifacts. Recently, research studies utilizing deep learning methods has made promising progress in removing artifacts for Sparse-View Computed Tomography (SVCT). However, given the limitations on the generalization capability of deep learning models, current methods usually train models on fixed sampling rates, affecting the usability and flexibility of model deployment in real clinical settings. To address this issue, our study proposes a adaptive reconstruction method to achieve high-performance SVCT reconstruction at any sampling rate. Specifically, we design a novel imaging degradation operator in the proposed sampling diffusion model for SVCT (CT-SDM) to simulate the projection process in the sinogram domain. Thus, the CT-SDM can gradually add projection views to highly undersampled measurements to generalize the full-view sinograms. By choosing an appropriate starting point in diffusion inference, the proposed model can recover the full-view sinograms from any sampling rate with only one trained model. Experiments on several datasets have verified the effectiveness and robustness of our approach, demonstrating its superiority in reconstructing high-quality images from sparse-view CT scans across various sampling rates.
A-SDM: Accelerating Stable Diffusion through Model Assembly and Feature Inheritance Strategies
Tsinghua University（清华大学）；Nankai University（南开大学）；Kuaishou Technology（快手科技）；Tianjin University of Finance and Economics（天津财经大学）；Imperial College London（伦敦帝国学院）
ReCo-Diff: Residual-Conditioned Deterministic Sampling for Cold Diffusion in Sparse-View CT
National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea（韩国全南数学研究所）；Department of Computer Engineering, Korea National University of Transportation, Chungju, 27469, Republic of Korea（韩国交通国立大学计算机工程系）
