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Proceedings Paper

Overcoming ill-posedness in optical tomography
Author(s): Andreas H. Hielscher; Sebastian Bartel
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Paper Abstract

In optical tomography (OT) one attempts to reconstruct cross- sectional images of various body parts given data from near- infrared transmission measurements. The cross-sectional images display the spatial distribution of optical properties, such as the absorption coefficient (mu) a, the reduced scattering coefficient (mu) s', or a combination thereof. One of the major problems of the novel imaging technology is that many different spatial distributions of optical properties inside the medium can lead to the same detector readings on the surface of the medium. Therefore, the reconstruction problem in optical tomography is ill posed. The choice of an appropriate method to overcome this problem is of crucial importance for any successful optical tomographic image reconstruction algorithm. In this work we approach the 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 a priori information about the system. We present the underlying concepts in our approach to overcome ill-posedness in optical tomography and show how different penalty terms affects the performance of the image reconstruction.

Paper Details

Date Published: 6 June 2000
PDF: 11 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387718
Show Author Affiliations
Andreas H. Hielscher, SUNY Downstate Medical Ctr. (United States)
Sebastian Bartel, SUNY Downstate Medical Ctr. (Germany)

Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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