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Proceedings Paper

A block-iterative deterministic annealing algorithm for Bayesian tomographic reconstruction
Author(s): Soo-Jin Lee
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Paper Abstract

We introduce a block iterative method to accelerate edge-preserving Bayesian reconstruction algorithms for emission tomography. Most common Bayesian approaches to tomographic reconstruction involve assumptions on the local spatial characteristics of the underlying source. To explicitly model the existence of anatomical boundaries, the line-process model has been often used. The unobservable binary line processes in this case acts to suspend smoothness constraints at sites where they are turned on. Deterministic annealing (DA) algorithms are known to provide an efficient means of handling the problems associated with mixed continuous and binary variable objectives. However, they are still computer intensive and require many iterations to converge. In this work, to further improve the DA algorithm by accelerating its convergence speed, we use a block-iterative (BI) method, which is derived from the ordered subset algorithm. The BI-DA algorithm processes the data in blocks within each iteration, thereby accelerating the convergence speed of a standard DA algorithm by a factor proportional to the number of blocks. The net conclusion is that, with moderate numbers of blocks and properly chosen hyperparameters, the BI-DA algorithm provides good reconstructions as well as a significant acceleration.

Paper Details

Date Published: 2 February 2006
PDF: 12 pages
Proc. SPIE 6065, Computational Imaging IV, 606517 (2 February 2006); doi: 10.1117/12.642198
Show Author Affiliations
Soo-Jin Lee, Paichai Univ. (South Korea)

Published in SPIE Proceedings Vol. 6065:
Computational Imaging IV
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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