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Related Experiment Videos

Approximation errors and model reduction in optical tomography.

V Kolehmainen1, S R Arridge, J P Kaipio

  • 1Dept. of Phys., Univ. Kuopio, Finland. Ville.Kolehmainen@uku.fi

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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Model reduction in optical diffusion tomography (ODT) is essential. Applying approximation error theory allows for sparser meshes, improving computational efficiency without sacrificing accuracy in ODT.

Area of Science:

  • Biomedical Optics
  • Computational Imaging
  • Inverse Problems

Background:

  • Model reduction is frequently necessary in optical diffusion tomography (ODT) due to computational constraints.
  • Limited computation time or memory often necessitates the use of sparse meshes for the forward problem in ODT.
  • Increasingly accurate measurements demand more precise forward problem solvers to fully utilize the data.

Purpose of the Study:

  • To apply approximation error theory to optical diffusion tomography (ODT).
  • To investigate the impact of estimating and employing approximation errors on mesh density in ODT.
  • To determine if ODT can utilize mesh densities previously considered unacceptable.

Main Methods:

  • Application of approximation error theory to the ODT model.

Related Experiment Videos

  • Analysis of the relationship between measurement accuracy and forward problem solver requirements.
  • Evaluation of mesh densities under varying approximation error conditions.

    • Main Results:

    • Demonstration that estimating and employing approximation errors is feasible in ODT.
    • Identification of conditions where sparser meshes are acceptable.
    • Quantification of the benefits of approximation error estimation for computational efficiency.

    Conclusions:

    • Approximation error theory provides a framework for optimizing mesh density in ODT.
    • The findings suggest a potential for improved computational efficiency in ODT without compromising data integrity.
    • This approach enables the use of less dense meshes, making ODT more accessible with limited computational resources.