15 July 1999 Use of a priori information and penalty terms in gradient-based iterative reconstruction schemes
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Proceedings Volume 3597, Optical Tomography and Spectroscopy of Tissue III; (1999) https://doi.org/10.1117/12.356847
Event: BiOS '99 International Biomedical Optics Symposium, 1999, San Jose, CA, United States
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andreas H. Hielscher, Andreas H. Hielscher, Alexander D. Klose, Alexander D. Klose, } "Use of a priori information and penalty terms in gradient-based iterative reconstruction schemes", Proc. SPIE 3597, Optical Tomography and Spectroscopy of Tissue III, (15 July 1999); doi: 10.1117/12.356847; https://doi.org/10.1117/12.356847
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