Parametric reconstruction method in optical tomography.

Xuejun Gu, Kui Ren, James Masciotti, Andreas H. Hielscher

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Optical tomography consists of reconstructing the spatial of a medium's optical properties from measurements of transmitted light on the boundary of the medium. Mathematically this problem amounts to parameter identification for the radiative transport equation (ERT) or diffusion approximation (DA). However, this type of boundary-value problem is highly ill-posed and the image reconstruction process is often unstable and non-unique. To overcome this problem, we present a parametric inverse method that considerably reduces the number of variables being reconstructed. In this way the amount of measured data is equal or larger than the number of unknowns. Using synthetic data, we show examples that demonstrate how this approach leads to improvements in imaging quality.

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Optical Tomography
Computer-Assisted Image Processing
Optical tomography
Radiative transfer
Image reconstruction
Boundary value problems
Identification (control systems)
Optical properties
Imaging techniques
Light

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Parametric reconstruction method in optical tomography.",
abstract = "Optical tomography consists of reconstructing the spatial of a medium's optical properties from measurements of transmitted light on the boundary of the medium. Mathematically this problem amounts to parameter identification for the radiative transport equation (ERT) or diffusion approximation (DA). However, this type of boundary-value problem is highly ill-posed and the image reconstruction process is often unstable and non-unique. To overcome this problem, we present a parametric inverse method that considerably reduces the number of variables being reconstructed. In this way the amount of measured data is equal or larger than the number of unknowns. Using synthetic data, we show examples that demonstrate how this approach leads to improvements in imaging quality.",
author = "Xuejun Gu and Kui Ren and James Masciotti and Hielscher, {Andreas H.}",
year = "2006",
language = "English (US)",
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AB - Optical tomography consists of reconstructing the spatial of a medium's optical properties from measurements of transmitted light on the boundary of the medium. Mathematically this problem amounts to parameter identification for the radiative transport equation (ERT) or diffusion approximation (DA). However, this type of boundary-value problem is highly ill-posed and the image reconstruction process is often unstable and non-unique. To overcome this problem, we present a parametric inverse method that considerably reduces the number of variables being reconstructed. In this way the amount of measured data is equal or larger than the number of unknowns. Using synthetic data, we show examples that demonstrate how this approach leads to improvements in imaging quality.

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