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Retracted

BioMed Research International has retracted this article. The article was previously submitted to arXiv as: Ondřej Tichý, Václav Šmídl, “Non-parametric Bayesian Models of Response Function in Dynamic Image Sequences,” arxiv, 2015 (https://arxiv.org/abs/1503.05684).

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References

  1. B. Shan, “Estimation of response functions based on variational bayes algorithm in dynamic images sequences,” BioMed Research International, vol. 2016, Article ID 4851401, 9 pages, 2016.
BioMed Research International
Volume 2016, Article ID 4851401, 9 pages
http://dx.doi.org/10.1155/2016/4851401
Research Article

Estimation of Response Functions Based on Variational Bayes Algorithm in Dynamic Images Sequences

School of Information Engineering, Chang’an University, Shaanxi 710064, China

Received 16 April 2016; Revised 8 June 2016; Accepted 17 July 2016

Academic Editor: Zexuan Ji

Copyright © 2016 Bowei Shan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We proposed a nonparametric Bayesian model based on variational Bayes algorithm to estimate the response functions in dynamic medical imaging. In dynamic renal scintigraphy, the impulse response or retention functions are rather complicated and finding a suitable parametric form is problematic. In this paper, we estimated the response functions using nonparametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness. These assumptions were used within hierarchical priors of the variational Bayes algorithm. We performed our algorithm on the real online dataset of dynamic renal scintigraphy. The results demonstrated that this algorithm improved the estimation of response functions with nonparametric priors.