A hybrid approach for diffuse optical tomography combining evolution strategies and gradient techniques

A. D. Klose, A. H. Hielscher

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    Diffuse optical tomography (DOT) can be considered as an optimization problem, in which the minimum of an objective function is sought. The objective function is typically some measure of the difference between the predicted and experimentally obtained detector readings. Most of the optimization techniques that are currently applied in optical tomography employ so-called gradient methods. These methods start from an initial guess of the distribution of optical properties and iteratively update this initial guess along the gradient of the objective function. It is well known that the success of gradient techniques depends strongly on the initial guess. If the guess is not chosen appropriately, the algorithm may not converge or may converge to a local minimum. Evolution strategies are global optimization techniques that depend much less on initial guesses. The drawback of evolution-based codes is that they are computationally expensive. In this work we introduce a hybrid approach that combines the advantages of gradient techniques and evolution strategies. The hybrid algorithm is less dependent on an initial guess and overcomes the computational burden connected to evolution strategies.

    Original languageEnglish (US)
    Pages (from-to)11-19
    Number of pages9
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Volume4250
    DOIs
    StatePublished - 2001
    EventOptical Tomography and Spectroscopy of Tissue IV - San Jose, CA, United States
    Duration: Jan 21 2001Jan 23 2001

    Keywords

    • Conjugate gradient technique
    • Evolution strategy
    • Global optimization
    • Image reconstruction
    • Optical tomography
    • Scattering media
    • Transport theory

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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