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Proceedings Paper

Use of a priori information and penalty terms in gradient-based iterative reconstruction schemes
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Paper Abstract

It is well known that the reconstruction problem in optical tomography is ill-posed. Therefore, the choice of an appropriate regularization method is of crucial importance for any successful image reconstruction algorithm. In this work we approach the regularization problem within a gradient-based image iterative reconstruction (GIIR) scheme. The image reconstruction is considered as a minimization of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain additional a priori information about the system. For the efficient minimization of this objective function the gradient with respect to the spatial distribution of optical properties is calculated. Besides presenting the underlying concepts in our approach to the regularization problem, we will show numerical results that demonstrate how prior knowledge can improve the reconstruction results.

Paper Details

Date Published: 15 July 1999
PDF: 9 pages
Proc. SPIE 3597, Optical Tomography and Spectroscopy of Tissue III, (15 July 1999); doi: 10.1117/12.356847
Show Author Affiliations
Andreas H. Hielscher, SUNY/Brooklyn Health Science Ctr. (United States)
Alexander D. Klose, SUNY/Brooklyn Health Science Ctr. (United States)


Published in SPIE Proceedings Vol. 3597:
Optical Tomography and Spectroscopy of Tissue III
Britton Chance; Robert R. Alfano; Bruce J. Tromberg, Editor(s)

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