This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales.
1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)
Institute for Information Transmission Problems – IITP RAS（俄罗斯科学院信息传输问题研究所）；Moscow Institute of Physics and Technology (National Research University)（莫斯科物理技术学院（国立研究型大学））
Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusion
School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne（计算机与通信科学学院，瑞士联邦理工学院）；School of Basic Sciences, École Polytechnique Fédérale de Lausanne（基础科学学院，瑞士联邦理工学院）；The School of Life Sciences, École Polytechnique Fédérale de Lausanne（生命科学学院，瑞士联邦理工学院）
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
National Digital Switching System Engineering and Technological Research Center（国家数字交换系统工程技术研究中心）；Tsinghua University（清华大学）；Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education（粒子与辐射成像教育部重点实验室（清华大学））
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
Inception Institute of Artificial Intelligence（起源人工智能研究院）；State Key Laboratory, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心国家重点实验室）；Baidu（百度）；South China University of Technology（华南理工大学）；National Scientific and Technical Research Council, CONICET（阿根廷国家科学与技术研究理事会）；Yatiris Group, PLADEMA Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN)（布宜诺斯艾利斯省中央国立大学）
