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Globally convergent algorithms for maximum a posteriori transmission tomography
1Dept. of Biostat., Michigan Univ., Ann Arbor, MI.
Summary
This study compares three maximum likelihood algorithms for transmission tomography, finding that convex and gradient algorithms are computationally superior to the EM algorithm due to fewer exponentiations.
Area of Science:
- Medical Imaging
- Computational Science
Background:
- Transmission tomography is a crucial imaging technique.
- Maximum likelihood algorithms are widely used for image reconstruction.
Purpose of the Study:
- To review and compare three maximum likelihood algorithms for transmission tomography.
- To evaluate the computational efficiency and convergence properties of these algorithms.
Main Methods:
- Comparison of Expectation-Maximization (EM) algorithm, De Pierro's convex algorithm, and an ad hoc gradient algorithm.
- Assessment of local and global convergence properties.
- Numerical testing on simulated transmission tomography data.
Main Results:
- The convex and gradient algorithms demonstrate computational superiority over the EM algorithm.
- This efficiency is attributed to the EM algorithm's higher number of exponentiations.
- Both convex and gradient algorithms exhibit desirable convergence properties and are suitable for Bayesian smoothing priors.
- The convex and gradient algorithms are well-suited for parallel computing.
Conclusions:
- The convex and gradient algorithms represent more computationally efficient alternatives to the EM algorithm for transmission tomography.
- These algorithms offer advantages in terms of speed and parallelization potential for image reconstruction tasks.
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