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- Charles A. Bouman, Ken D. Sauer
- IEEE Trans. Image Processing
- 1993

The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies… (More)

- Ken D. Sauer, Charles A. Bouman
- IEEE Trans. Signal Processing
- 1993

1 Iterative methods for statistically-based reconstruction from projections are computationally costly relative to convolution backprojection, but allow useful image reconstruction from sparse and noisy data. We present a method for Bayesian reconstruction which relies on updates of single pixel values, rather than the entire image, at each iteration. The… (More)

- Jean-Baptiste Thibault, Ken D Sauer, Charles A Bouman, Jiang Hsieh
- Medical physics
- 2007

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical… (More)

- Charles A. Bouman, Ken D. Sauer
- IEEE Trans. Image Processing
- 1996

Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem,… (More)

- Suhail S. Saquib, Charles A. Bouman, Ken D. Sauer
- IEEE Trans. Image Processing
- 1998

Markov random fields (MRF's) have been widely used to model images in Bayesian frameworks for image reconstruction and restoration. Typically, these MRF models have parameters that allow the prior model to be adjusted for best performance. However, optimal estimation of these parameters(sometimes referred to as hyper parameters) is difficult in practice for… (More)

- Mustafa E. Kamasak, Charles A. Bouman, Evan D. Morris, Ken D. Sauer
- IEEE Trans. Med. Imaging
- 2005

Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the… (More)

- Ken Sauer, Charles Bouman
- 1992

We present a method for nondifferentiable optimization in MAP estimation of computed transmission tomograms. This problem arises in the application of a Markov random field image model with absolute value potential functions. Even though the required optimization is on a convex function, local optimization methods, which iteratively update pixel values,… (More)

- Ken D. Sauer, S. Borman, Charles A. Bouman
- ICIP
- 1995

While Bayesian methods can significantly improve the quality of tomographic reconstructions, they require the solution of large iterative optimization problems. Recent results indicate that the convergence of these optimization problems can be improved by using sequential pixel updates, or Gauss-Seidel iterations. However, Gauss-Seidel iterations may be… (More)

- Zhou Yu, Jean-Baptiste Thibault, Charles A. Bouman, Ken D. Sauer, Jiang Hsieh
- IEEE Transactions on Image Processing
- 2011

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical… (More)

Statistical methods of discrete tomographic reconstruction pose new problems both in stochastic modeling to define an optimal reconstruction, and in optimization to find that reconstruction. Multiscale models have succeeded in improving representation of structure of varying scale in imagery, a chronic problem for common Markov random fields. This chapter… (More)