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Related Concept Videos

Computed Tomography01:10

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Related Experiment Video

Updated: Dec 21, 2025

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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Improved algorithm with adaptive regularization for tomographic reconstruction of gas distributions using DOAS

N Casaballe, M Di Martino, M Osorio

    Applied Optics
    |May 14, 2020
    PubMed
    Summary

    This study introduces an adaptive algorithm for atmospheric trace gas analysis using multi-axis DOAS. The method improves tomographic reconstructions of gas distributions, like nitrogen dioxide (NO2), by accounting for uneven sampling.

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    Area of Science:

    • Atmospheric chemistry and physics
    • Spectroscopic analysis
    • Environmental monitoring

    Background:

    • Differential Optical Absorption Spectroscopy (DOAS) is effective for detecting atmospheric trace gases.
    • Tomographic reconstruction can map gas distributions from multiple observation points.
    • Non-uniform spatial sampling poses challenges for accurate gas plume analysis.

    Purpose of the Study:

    • To develop and test a novel algorithm for tomographic reconstruction of atmospheric trace gas distributions.
    • To address challenges posed by non-homogeneous spatial sampling in multi-axis DOAS.
    • To improve the accuracy of retrieving gas distributions from emission sources.

    Main Methods:

    • Utilized multi-axis DOAS observations for tomographic reconstruction.
    • Developed a new algorithm based on regularized minimization.
    • Introduced an adaptive approach with locally tuned regularization weights based on sampling uncertainties.
    • Validated the method using simulated gas distributions and experimental data.

    Main Results:

    • The adaptive algorithm successfully reconstructed simulated gas distributions.
    • The approach effectively handled non-homogeneous spatial sampling.
    • Demonstrated accurate retrieval of nitrogen dioxide (NO2) distribution in a plume cross-section.

    Conclusions:

    • The proposed adaptive regularization approach enhances the accuracy of tomographic gas distribution retrieval.
    • This method offers a robust solution for analyzing atmospheric plumes with multi-axis DOAS.
    • The technique is valuable for environmental monitoring and understanding emission source impacts.