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Proceedings Paper

Approach to estimating low contrast inclusion with a priori guidance
Author(s): Min-Chun Pan; Chien-Hung Chen; Liang-Yu Chen; Min-Cheng Pan
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Paper Abstract

Near-infrared diffuse optical tomography (NIR DOT) for noninvasive tissue monitoring have been developed for nearly two decades. The NIR imaging, however, suffers from low resolution due to the diffusive nature of the scattered light; there are compelling reasons for merging high-resolution structural information from other imaging modalities with the functional information attainable with NIR DOT. In this article, slight variation of the inclusion (tumor) in low contrast of optical properties is estimated and investigated. We present that an initial study of using a structural a prioriknowledge in NIR tomography where absorption image reconstruction of the tested phantom is well defined with the aid of a structural a priori knowledge obtained from other imaging modalities. This is advantageous compared to either modality alone. As well, the reconstructed optical absorption coefficient is achieved more accurate near to be exact value with incorporating the empirical updating information being proportional to the off-boundary distance but not size of inclusion against the background. Numerical simulation is demonstrated on varied sizes, locations and contrast of the inclusion. With the comparison between with or without a priori and empirical updating information, it is found that the reconstructed optical properties are more accurate than the near-infrared imaging alone.

Paper Details

Date Published: 12 July 2007
PDF: 7 pages
Proc. SPIE 6629, Diffuse Optical Imaging of Tissue, 66291U (12 July 2007); doi: 10.1117/12.728065
Show Author Affiliations
Min-Chun Pan, National Central Univ. (Taiwan)
Chien-Hung Chen, National Central Univ. (Taiwan)
Liang-Yu Chen, National Central Univ. (Taiwan)
Min-Cheng Pan, Tung-Nan Institute of Technology (Taiwan)

Published in SPIE Proceedings Vol. 6629:
Diffuse Optical Imaging of Tissue
Brian W. Pogue; Rinaldo Cubeddu, Editor(s)

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