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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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3D simulations: true maps, K-Bayes, zDFT, and Hamming windowed zDFT reconstructions for a set of four slices at the higher-resolution of structural MRI. Visually, K-Bayes reconstruction does a much better job of reproducing the truth and nicely captures changes over the four slices.
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Fig4: 3D simulations: true maps, K-Bayes, zDFT, and Hamming windowed zDFT reconstructions for a set of four slices at the higher-resolution of structural MRI. Visually, K-Bayes reconstruction does a much better job of reproducing the truth and nicely captures changes over the four slices.

Mentions: The reconstructions of 3D K-Bayes and zDFT for the 128 × 128 × 128 volume are displayed in Figure 4. Four adjacent slices of the high-resolution simulated truth are shown alongside the corresponding four reconstructed slices from K-Bayes and Hamming windowed zDFT. The K-Bayes reconstruction not only provides much sharper definition than the zDFT reconstruction but also allows for variable changes in spatial pattern across the finer width MRI slices. For zDFT, the best that could be done was to interpolate the slices. Table 2 displays quantitative summaries for the 3D simulation study. Gray matter bias was reduced by 60% for K-Bayes compared with zDFT, though white matter bias was almost 50% higher in K-Bayes. Hamming had even better white matter bias than zDFT and K-Bayes, but considerably worse gray matter bias. The apparent improvement in white matter bias for the DFT-based approaches is likely artifactual, the chance consequence of two competing errors. First, there is a tendency for partial volume effects to merge signal between gray and white matter, thereby increasing the white matter level and reducing the gray. Second, the lack of signal outside the brain causes partial volume effects that reduce the white matter signal, to some degree canceling the first error. K-Bayes reduces the influence of the first partial volume effect because only light smoothness is modeled across gray/white boundaries. However, K-Bayes is not as susceptible to the second partial volume effect because of the strong prior modeling of a lack of signal in non-brain tissue. Thus, there is no competing error effect in K-Bayes. Furthermore, the RMSE drops from 0.47 for the DFT reconstruction down to 0.28 for the K-Bayes estimate, showing that K-Bayes reduced the “average” error by over 25%. The hotspot bias and RMSE were both considerably reduced with K-Bayes relative to zDFT or Hamming.Fig 4


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)

3D simulations: true maps, K-Bayes, zDFT, and Hamming windowed zDFT reconstructions for a set of four slices at the higher-resolution of structural MRI. Visually, K-Bayes reconstruction does a much better job of reproducing the truth and nicely captures changes over the four slices.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2865632&req=5

Fig4: 3D simulations: true maps, K-Bayes, zDFT, and Hamming windowed zDFT reconstructions for a set of four slices at the higher-resolution of structural MRI. Visually, K-Bayes reconstruction does a much better job of reproducing the truth and nicely captures changes over the four slices.
Mentions: The reconstructions of 3D K-Bayes and zDFT for the 128 × 128 × 128 volume are displayed in Figure 4. Four adjacent slices of the high-resolution simulated truth are shown alongside the corresponding four reconstructed slices from K-Bayes and Hamming windowed zDFT. The K-Bayes reconstruction not only provides much sharper definition than the zDFT reconstruction but also allows for variable changes in spatial pattern across the finer width MRI slices. For zDFT, the best that could be done was to interpolate the slices. Table 2 displays quantitative summaries for the 3D simulation study. Gray matter bias was reduced by 60% for K-Bayes compared with zDFT, though white matter bias was almost 50% higher in K-Bayes. Hamming had even better white matter bias than zDFT and K-Bayes, but considerably worse gray matter bias. The apparent improvement in white matter bias for the DFT-based approaches is likely artifactual, the chance consequence of two competing errors. First, there is a tendency for partial volume effects to merge signal between gray and white matter, thereby increasing the white matter level and reducing the gray. Second, the lack of signal outside the brain causes partial volume effects that reduce the white matter signal, to some degree canceling the first error. K-Bayes reduces the influence of the first partial volume effect because only light smoothness is modeled across gray/white boundaries. However, K-Bayes is not as susceptible to the second partial volume effect because of the strong prior modeling of a lack of signal in non-brain tissue. Thus, there is no competing error effect in K-Bayes. Furthermore, the RMSE drops from 0.47 for the DFT reconstruction down to 0.28 for the K-Bayes estimate, showing that K-Bayes reduced the “average” error by over 25%. The hotspot bias and RMSE were both considerably reduced with K-Bayes relative to zDFT or Hamming.Fig 4

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