Approach to estimating low contrast inclusion with a priori guidance

Min Chun Pan, Chien Hung Chen, Liang Yu Chen, Min Cheng Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 priori knowledge 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.

Original languageEnglish
Title of host publicationDiffuse Optical Imaging of Tissue
StatePublished - 2007
EventDiffuse Optical Imaging of Tissue - Munich, Germany
Duration: 19 Jun 200721 Jun 2007

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceDiffuse Optical Imaging of Tissue


  • Diffuse optical tomography (DOT)
  • Empirical updating information
  • Image reconstruction
  • Near-infrared (NIR)
  • Structural a priori knowledge


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