This study presents a technique for 2D tomography under unknown viewing angles when the distribution of the viewing angles is also unknown. Unknown view tomography (UVT) is a problem encountered in cryo-electron microscopy and in the geometric calibration of CT systems. There exists a moderate-sized literature on the 2D UVT problem, but most existing 2D UVT algorithms assume knowledge of the angle distribution which is not available usually. Our proposed methodology formulates the problem as an optimization task based on cross-validation error, to estimate the angle distribution jointly with the underlying 2D structure in an alternating fashion. We explore the algorithm's capabilities for the case of two probability distribution models: a semi-parametric mixture of von Mises densities and a probability mass function model. We evaluate our algorithm's performance under noisy projections using a PCA-based denoising technique and Graph Laplacian Tomography (GLT) driven by order statistics of the estimated distribution, to ensure near-perfect ordering, and compare our algorithm to intuitive baselines.
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）
