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Proceedings Paper

Image reconstruction performance as a function of model complexity using information geometry: application to transmission tomographic imaging
Author(s): Joseph A. O'Sullivan; Liangjun Xie; David G. Politte; Bruce R. Whiting
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Paper Abstract

Models used to derive image reconstruction algorithms typically make assumptions designed to increase the computational tractability of the algorithms while taking enough account of the physics to achieve desired performance. As the models for the physics become more detailed, the algorithms typically increase in complexity, often due to increases in the number of parameters in the models. When parameters are estimated from measured data and models of increased complexity include those of lower complexity as special cases, then as the number of parameters increases, model errors decrease and estimation errors increase. We adopt an information geometry approach to quantify the loss due to model errors and Fisher information to quantify the loss due to estimation errors. These are unified into one cost function. This approach is detailed in an X-ray transmission tomography problem where allmodels are approximations to the underlying problem defined on the continuum. Computations and simulations demonstrate the approach. The analysis provides tools for determining an appropriate model complexity for a given problem and bounds on information that can be extracted.

Paper Details

Date Published: 28 February 2007
PDF: 12 pages
Proc. SPIE 6498, Computational Imaging V, 649806 (28 February 2007); doi: 10.1117/12.716264
Show Author Affiliations
Joseph A. O'Sullivan, Washington Univ. (United States)
Liangjun Xie, Washington Univ. (United States)
David G. Politte, Washington Univ. School of Medicine (United States)
Bruce R. Whiting, Washington Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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