Fast image reconstruction techniques are becoming important with the increasing number of scientific cases in high resolution micro and nano tomography. The processing of the large scale three-dimensional data demands new mathematical tools for the tomographic reconstruction task because of the big computational complexity of most current algorithms as the sizes of tomographic data grow with the development of more powerful acquisition hardware and more refined scientific needs. In the present paper we propose a new fast back-projection operator for the processing of tomographic data and compare it against other fast reconstruction techniques.
Institute of Biomathematics and Biometry, Helmholtz Zentrum München, German Research Center for Environmental Health（生物数学与生物计量研究所，赫尔霍兹慕尼黑中心，德国环境健康研究中心）；Technische Universität München（慕尼黑工业大学）
Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation
Institute of Natural Sciences, Shanghai Jiao Tong University（上海交通大学自然科学研究院）；Beijing International Center for Mathematical Research Peking University（北京大学北京国际数学研究中心）；Institute of Natural Sciences, School of Mathematical Sciences, and MOE-LSC, Shanghai Jiao Tong University（上海交通大学自然科学研究院、数学科学学院和教育部科学计算与工程应用重点实验室）
Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry
Research Unit of Mathematical Sciences, University of Oulu（奥卢大学数学科学研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；Department of Mathematics, University of Arizona（亚利桑那大学数学系）；Department of Technical Physics, University of Eastern Finland（东部芬兰大学技术物理系）
