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Benchmark evaluation of inversion algorithms for tomographic absorption spectroscopy.

Tao Yu, Weiwei Cai

    Applied Optics
    |April 5, 2017
    PubMed
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    This study compares inversion algorithms for Tomographic Absorption Spectroscopy (TAS). Landweber and MLEM are robust for rank-deficient problems, while TSVD excels with discrete ill-posed problems, offering insights for various tomographic methods.

    Area of Science:

    • Optics and Spectroscopy
    • Image Reconstruction
    • Computational Imaging

    Background:

    • Tomographic Absorption Spectroscopy (TAS) is gaining interest due to advancements in laser technology and imaging.
    • Existing TAS algorithms are adapted from medical imaging and process control, but lack systematic comparison.
    • Rank-deficient (RD) and discrete ill-posed (DIP) problems are common challenges in TAS.

    Purpose of the Study:

    • To systematically compare major inversion algorithms for TAS.
    • To evaluate algorithm performance on both RD and DIP problems.
    • To investigate control parameter determination and semiconvergence in iterative methods.

    Main Methods:

    • Comparative analysis of inversion algorithms including Landweber, Maximum Likelihood Expectation Maximization (MLEM), and Truncated Singular Value Decomposition (TSVD).

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  • Testing algorithms on extensive cases with three representative phantoms.
  • Analysis of control parameter settings and semiconvergent behavior of iterative methods.

    • Main Results:

    • Landweber algorithm outperformed others in two RD cases and was comparable to MLEM in a third.
    • Landweber and MLEM demonstrated better immunity to semiconvergence than other methods.
    • TSVD is recommended for DIP problems due to its noise suppression capabilities.

    Conclusions:

    • The Landweber algorithm offers robustness for RD problems in TAS.
    • TSVD is the preferred method for DIP problems in TAS, effectively managing noise.
    • Findings provide valuable insights for TAS and potentially other tomographic imaging modalities.