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- W1619490064 abstract "The dynamic image reconstruction allows to combine the acquisition of projection data with the reconstruction task. The image is supposed to be a random field discrete in space. Taking into account the properties of the acquisition task, the data can be represented as a multi-dimensional discrete-time Markovian process. As a result the Kalman filter technique in the time domain is proposed for optimal linear dynamic reconstruction. The main restrictions for employing the Kalman filter approach consist in the huge dimension of the algorithm and the strong requirements to the introduced prior knowledge. The main goal of this paper is to investigate a pseudo Kalman filter algorithm that provides sufficient decrease of the dimension and reduction of the necessary amount of prior knowledge. The proposed approach is based on applying the regularization principle together with a recursive estimation procedure. The regularization principle allows to replace statistical prior by empirical prior. The appropriate convergence condition of the pseudo Kalman filter algorithm is obtained by introducing the regularization function. To increase the convergence velocity an optimal data acquisition procedure is proposed. The potential of the proposed algorithms are discussed in terms of reconstruction results for glass fiber cable and dynamic fields." @default.
- W1619490064 created "2016-06-24" @default.
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- W1619490064 date "2001-01-01" @default.
- W1619490064 modified "2023-09-27" @default.
- W1619490064 title "Dynamic image reconstruction for CT applications" @default.
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- W1619490064 doi "https://doi.org/10.1063/1.1373832" @default.
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