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Tomographic reconstruction of flowing gases using sparse training

Zeeshan Nadir,M. S. Brown,M. Comer,C. Bouman

Published 2014 in International Conference on Information Photonics

ABSTRACT

Tunable Diode Laser Absorption Spectroscopy (TDLAS) is an emerging technique for simultaneous sensing of temperature and concentration of gaseous media. However, simultaneous reconstruction of temperature and concentration using TDLAS measurements is a nonlinear inverse problem and unlike other forms of computed tomography (CT), it is typically not possible to take a large number of projection measurements; so reconstructions are often computed using simplistic assumptions that limit the usability of the results. In this paper, we present a fast algorithm for model-based iterative reconstruction (MBIR) of TDLAS data. Our TDLAS-MBIR method uses a nonlinear forward model based on the physics of light absorption and incorporates a holistic prior model that can be learned from very sparse training data. Reconstructions performed on computational fluid dynamics (CFD) phantoms show that our proposed reconstruction algorithm is fast; works well when the number of pixels, p, far exceeds the number of measurements, M; is robust against noise; and produces good reconstructions using few training examples for the prior model.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    International Conference on Information Photonics

  • Publication date

    2014-10-01

  • Fields of study

    Physics, Computer Science, Engineering

  • Identifiers
    DOI 10.1109/ICIP.2014.7025347
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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