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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.

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Reconstructions from K-Bayes, zDFT, and zDFT of Hamming windowed data. K-Bayes provides the most detailed reconstruction and recaptures many higher resolution features lost in the DFT-based reconstruction.
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Fig6: Reconstructions from K-Bayes, zDFT, and zDFT of Hamming windowed data. K-Bayes provides the most detailed reconstruction and recaptures many higher resolution features lost in the DFT-based reconstruction.

Mentions: The results of the different reconstructions are shown in Figure 6. K-Bayes clearly provides the best visual reconstruction of the three approaches. It presents the most contrast and captures more of the gold standard structure. The numerical comparisons in Table 3 indicate that K-Bayes improves over the other methods for all metrics. In particular, bias is around one fourth of that for zDFT, and the gray/white effect size is 50% higher. The RMSE did not show the level of improvements for K-Bayes that were observed in the simulation studies. We believe that there are two reasons for this. First the (pseudo) gold standard is expanded via zero filling from a small enough region of k-space such that it contains artifacts of Gibbs ringing and aliasing. K-Bayes reconstruction of the further reduced dataset does not reproduce these artifacts, whereas the zDFT reconstruction does. Therefore, the RMSE of zDFT would increase and that of K-Bayes would decrease when compared to a true gold standard that did not contain Gibbs ringing and aliasing artifacts. Second, K-Bayes reduces noise that exists in the gold standard. Unlike the simulation study, the gold standard here contains noise. A definitive evaluation would require high-resolution and low-noise perfusion MRI to be used as a gold standard, but this is currently not available as a standard acquisition procedure.Fig 6


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)

Reconstructions from K-Bayes, zDFT, and zDFT of Hamming windowed data. K-Bayes provides the most detailed reconstruction and recaptures many higher resolution features lost in the DFT-based reconstruction.
© Copyright Policy
Related In: Results  -  Collection

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

Fig6: Reconstructions from K-Bayes, zDFT, and zDFT of Hamming windowed data. K-Bayes provides the most detailed reconstruction and recaptures many higher resolution features lost in the DFT-based reconstruction.
Mentions: The results of the different reconstructions are shown in Figure 6. K-Bayes clearly provides the best visual reconstruction of the three approaches. It presents the most contrast and captures more of the gold standard structure. The numerical comparisons in Table 3 indicate that K-Bayes improves over the other methods for all metrics. In particular, bias is around one fourth of that for zDFT, and the gray/white effect size is 50% higher. The RMSE did not show the level of improvements for K-Bayes that were observed in the simulation studies. We believe that there are two reasons for this. First the (pseudo) gold standard is expanded via zero filling from a small enough region of k-space such that it contains artifacts of Gibbs ringing and aliasing. K-Bayes reconstruction of the further reduced dataset does not reproduce these artifacts, whereas the zDFT reconstruction does. Therefore, the RMSE of zDFT would increase and that of K-Bayes would decrease when compared to a true gold standard that did not contain Gibbs ringing and aliasing artifacts. Second, K-Bayes reduces noise that exists in the gold standard. Unlike the simulation study, the gold standard here contains noise. A definitive evaluation would require high-resolution and low-noise perfusion MRI to be used as a gold standard, but this is currently not available as a standard acquisition procedure.Fig 6

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