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K-Bayes reconstruction for perfusion MRI I: concepts and application.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its red...

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Published in:Journal of Digital Imaging Vol. 23; no. 3; pp. 277 - 287
Main Authors: Kornak J, Young K, Schuff N, Du A, Maudsley A, Weiner M
Format: diagnostic images equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Jun2010
Online Access:View this record in EBSCOhost
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      dt: Jun2010
      vid: 23
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      pub: Springer Nature
      place: New York, New York
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        2010656977
        10.1007/s10278-009-9183-y
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        atl: K-Bayes reconstruction for perfusion MRI I: concepts and application.
      aug:
        au:
          Kornak J
          Young K
          Schuff N
          Du A
          Maudsley A
          Weiner M
        affil: Department of Radiology and Biomedical Imaging, University of California, San Francisco, 4150 Clement Street (114M), San Francisco, CA 94121, USA.
      sug:
        subj:
          Magnetic Resonance Imaging Methods
          Radiographic Image Enhancement Methods
          Computer Simulation
          Funding Source
          Human
          Statistics
          Validation Studies
      ab: Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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