Use of a priori information and penalty terms in gradient-based iterative reconstruction schemes

Andreas H. Hielscher, Alexander D. Klose

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    It is well known that the reconstruction problem in optical tomography is ill-posed. Therefore, the choice of an appropriate regularization method is of crucial importance for any successful image reconstruction algorithm. In this work we approach the regularization problem within a gradient-based image iterative reconstruction (GIIR) scheme. The image reconstruction is considered as a minimization of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain additional a priori information about the system. For the efficient minimization of this objective function the gradient with respect to the spatial distribution of optical properties is calculated. Besides presenting the underlying concepts in our approach to the regularization problem, we will show numerical results that demonstrate how prior knowledge can improve the reconstruction results.

    Original languageEnglish (US)
    Pages (from-to)36-44
    Number of pages9
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Volume3597
    StatePublished - 1999
    EventProceedings of the 1999 Optical Tomography and Spectroscopy of Tissue III - San Jose, CA, USA
    Duration: Jan 24 1999Jan 28 1999

    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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