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Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution.

Publication ,  Journal Article
Kornak, J; Young, K; Soher, BJ; Maudsley, AA
Published in: IEEE Trans Med Imaging
July 2010

A k-space-time Bayesian statistical reconstruction method (K-Bayes) is proposed for the reconstruction of metabolite images of the brain from proton (1H) magnetic resonance (MR) spectroscopic imaging (MRSI) data. K-Bayes performs full spectral fitting of the data while incorporating structural (anatomical) spatial information through the prior distribution. K-Bayes provides increased spatial resolution over conventional discrete Fourier transform (DFT) based methods by incorporating structural information from higher resolution coregistered and segmented structural MR images. The structural information is incorporated via a Markov random field (MRF) model that allows for differential levels of expected smoothness in metabolite levels within homogeneous tissue regions and across tissue boundaries. By further combining the structural prior model with a k -space-time MRSI signal and noise model (for a specific set of metabolites and based on knowledge from prior spectral simulations of metabolite signals), the impact of artifacts generated by low-resolution sampling is also reduced. The posterior-mode estimates are used to define the metabolite map reconstructions, obtained via a generalized expectation-maximization algorithm. K-Bayes was tested using simulated and real MRSI datasets consisting of sets of k-space-time-series (the recorded free induction decays). The results demonstrated that K-Bayes provided qualitative and quantitative improvement over DFT methods.

Duke Scholars

Published In

IEEE Trans Med Imaging

DOI

EISSN

1558-254X

Publication Date

July 2010

Volume

29

Issue

7

Start / End Page

1333 / 1350

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Magnetic Resonance Spectroscopy
  • Magnetic Resonance Imaging
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans
  • Brain
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Kornak, J., Young, K., Soher, B. J., & Maudsley, A. A. (2010). Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution. IEEE Trans Med Imaging, 29(7), 1333–1350. https://doi.org/10.1109/TMI.2009.2037956
Kornak, John, Karl Young, Brian J. Soher, and Andrew A. Maudsley. “Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution.IEEE Trans Med Imaging 29, no. 7 (July 2010): 1333–50. https://doi.org/10.1109/TMI.2009.2037956.
Kornak J, Young K, Soher BJ, Maudsley AA. Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution. IEEE Trans Med Imaging. 2010 Jul;29(7):1333–50.
Kornak, John, et al. “Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution.IEEE Trans Med Imaging, vol. 29, no. 7, July 2010, pp. 1333–50. Pubmed, doi:10.1109/TMI.2009.2037956.
Kornak J, Young K, Soher BJ, Maudsley AA. Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution. IEEE Trans Med Imaging. 2010 Jul;29(7):1333–1350.

Published In

IEEE Trans Med Imaging

DOI

EISSN

1558-254X

Publication Date

July 2010

Volume

29

Issue

7

Start / End Page

1333 / 1350

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Magnetic Resonance Spectroscopy
  • Magnetic Resonance Imaging
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans
  • Brain