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

Kornak J, Young K, Schuff N, Du A, Maudsley AA, Weiner MW - J Digit Imaging (2009)

Bottom Line: 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.A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT.The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, 4150 Clement Street (114M), San Francisco, CA 94121, USA. john.kornak@ucsf.edu

ABSTRACT
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.

Show MeSH
Real perfusion MRI analysis: high-resolution (32 × 64) perfusion MRI map is discrete Fourier-transformed into k-space and the center 16 × 16 region is cut out. The reduced k-space data are reconstructed using K-Bayes to high resolution (128 × 128). The K-Bayes reconstruction is then compared with a zDFT reconstruction from the 16 × 16 data. Gold standard is the 32 × 64 perfusion MRI map interpolated to 128 × 128.
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Fig5: Real perfusion MRI analysis: high-resolution (32 × 64) perfusion MRI map is discrete Fourier-transformed into k-space and the center 16 × 16 region is cut out. The reduced k-space data are reconstructed using K-Bayes to high resolution (128 × 128). The K-Bayes reconstruction is then compared with a zDFT reconstruction from the 16 × 16 data. Gold standard is the 32 × 64 perfusion MRI map interpolated to 128 × 128.

Mentions: K-Bayes reconstruction was performed on 4Β Tesla perfusion MRI data to demonstrate feasibility in real applications. Figure 5 displays the procedures used to perform and evaluate the different reconstruction techniques. Institutional Review Board approval had been obtained for this data which was acquired as part of a larger study.FigΒ 5


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

Kornak J, Young K, Schuff N, Du A, Maudsley AA, Weiner MW - J Digit Imaging (2009)

Real perfusion MRI analysis: high-resolution (32 × 64) perfusion MRI map is discrete Fourier-transformed into k-space and the center 16 × 16 region is cut out. The reduced k-space data are reconstructed using K-Bayes to high resolution (128 × 128). The K-Bayes reconstruction is then compared with a zDFT reconstruction from the 16 × 16 data. Gold standard is the 32 × 64 perfusion MRI map interpolated to 128 × 128.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865632&req=5

Fig5: Real perfusion MRI analysis: high-resolution (32 × 64) perfusion MRI map is discrete Fourier-transformed into k-space and the center 16 × 16 region is cut out. The reduced k-space data are reconstructed using K-Bayes to high resolution (128 × 128). The K-Bayes reconstruction is then compared with a zDFT reconstruction from the 16 × 16 data. Gold standard is the 32 × 64 perfusion MRI map interpolated to 128 × 128.
Mentions: K-Bayes reconstruction was performed on 4Β Tesla perfusion MRI data to demonstrate feasibility in real applications. Figure 5 displays the procedures used to perform and evaluate the different reconstruction techniques. Institutional Review Board approval had been obtained for this data which was acquired as part of a larger study.FigΒ 5

Bottom Line: 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.A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT.The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, 4150 Clement Street (114M), San Francisco, CA 94121, USA. john.kornak@ucsf.edu

ABSTRACT
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.

Show MeSH