Qiu Wang
Siemens
43 Papers
269 Citations
Qiu Wang is an academic researcher from Siemens. The author has contributed to research in topics: Iterative reconstruction & Expectation–maximization algorithm. The author has an hindex of 11, co-authored 43 publications. Previous affiliations of Qiu Wang include Princeton University & Cornell University.
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Papers
A low dose simulation tool for CT systems with energy integrating detectors.
TL;DR: The proposed method can accurately simulate low-dose CT data starting from high-dose data, including effects from photon starvation and detector noise, and is shown to be more accurate in achieving the correct mean and variance in reconstructed images from pure-Poisson noise simulations.
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Dynamics in cryo EM reconstructions visualized with maximum-likelihood derived variance maps.
TL;DR: A quantitative method for simultaneously computing a reconstruction of the particle and a map of the space-varying heterogeneity of the particles based on an entire data set using a maximum likelihood algorithm that explicitly takes into account the continuous variability from one instance to another instance of a particle.
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Virus Assembly and Maturation: Auto-Regulation through Allosteric Molecular Switches
Tatiana Domitrovic,Navid Movahed,Brian Bothner,Tsutomu Matsui,Qiu Wang,Peter C. Doerschuk,John E. Johnson +6 more
TL;DR: There is a close similarity between the concepts of tensegrity and allostery (associated with geodesic domes and mechanical engineering) and allosteric communication among the four quasi-equivalent subunits in the icosahedral asymmetric unit.
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Three-dimensional reconstruction of the statistics of heterogeneous objects from a collection of one projection image of each object.
TL;DR: An estimation problem for statistical reconstruction of heterogeneous three-dimensional objects from two-dimensional tomographic data (single-particle cryoelectron microscope images) is posed as the problem of estimating class probabilities, means, and covariances for a Gaussian mixture where both the mean and covariance are stochastically structured.
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Patent
Robust subspace recovery via dual sparsity pursuit
TL;DR: In this paper, a dual sparse model framework is used to detect a foreground data in an image sequence using a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix.
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