Estimating posterior image variance with sparsity-based object priors for MRI

Yujia Chen, Yang Lou, Cihat Eldeniz, Hongyu An, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Joseph Y. Lo, Thomas G. Flohr
PublisherSPIE
ISBN (Electronic)9781510607095
DOIs
StatePublished - Jan 1 2017
EventMedical Imaging 2017: Physics of Medical Imaging - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10132
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Physics of Medical Imaging
CountryUnited States
CityOrlando
Period02/13/1702/16/17

Keywords

  • Magnetic resonance imaging
  • Sparse object model
  • Uncertainty estimation
  • Variational Bayesian inference

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  • Cite this

    Chen, Y., Lou, Y., Eldeniz, C., An, H., & Anastasio, M. A. (2017). Estimating posterior image variance with sparsity-based object priors for MRI. In T. G. Schmidt, J. Y. Lo, & T. G. Flohr (Eds.), Medical Imaging 2017: Physics of Medical Imaging [101321J] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10132). SPIE. https://doi.org/10.1117/12.2255555