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Proceedings Paper

Estimating posterior image variance with sparsity-based object priors for MRI
Author(s): Yujia Chen; Yang Lou; Cihat Eldeniz; Hongyu An; Mark A. Anastasio
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Paper Abstract

Point estimates, such as the maximum a posteriori (MAP) estimate, are commonly computed in image re- construction tasks. However, such point estimates provide no information about the range of highly probable solutions, namely the uncertainty in the computed estimate. Bayesian inference methods that seek to compute the posterior probability distribution function (PDF) of the object can provide exactly this information, but are generally computationally intractable. Markov Chain Monte Carlo (MCMC) methods, which avoid explicit posterior computation by directly sampling from the PDF, require considerable expertise to run in a proper way. This work investigates a computationally efficient variational Bayesian inference approach for computing the posterior image variance with application to MRI. The methodology employs a sparse object prior model that is consistent with the model assumed in most sparse reconstruction methods. The posterior variance map generated by the proposed method provides valuable information that reveals how data-acquisition parameters and the specification of the object prior affect the reliability of a reconstructed MAP image. The proposed method is demonstrated by use of computer-simulated MRI data.

Paper Details

Date Published: 9 March 2017
PDF: 6 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321J (9 March 2017); doi: 10.1117/12.2255555
Show Author Affiliations
Yujia Chen, Washington Univ. in St. Louis (United States)
Yang Lou, Washington Univ. in St. Louis (United States)
Cihat Eldeniz, Washington Univ. School of Medicine in St. Louis (United States)
Hongyu An, Washington Univ. School of Medicine in St. Louis (United States)
Mark A. Anastasio, Washington Univ. in St. Louis (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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