Overcoming ill-posedness in optical tomography

Andreas H. Hielscher, Sebastian Bartel

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    In optical tomography (OT) one attempts to reconstruct cross-sectional images of various body parts given data from near-infrared transmission measurements. The cross-sectional images display the spatial distribution of optical properties, such as the absorption coefficient μa, the reduced scattering coefficient μs′, or a combination thereof. One of the major problems of the novel imaging technology is that many different spatial distributions of optical properties inside the medium can lead to the same detector readings on the surface of the medium. Therefore, the reconstruction problem in optical tomography is ill posed. The choice of an appropriate method to overcome this problem is of crucial importance for any successful optical tomographic image reconstruction algorithm. In this work we approach the 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 a priori information about the system. We present the underlying concepts in our approach to overcome ill-posedness in optical tomography and show how different penalty terms affects the performance of the image reconstruction.

    Original languageEnglish (US)
    Pages (from-to)I/-
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Volume3979
    StatePublished - 2000
    EventMedical Imaging 2000: Image Processing - San Diego, CA, USA
    Duration: Feb 14 2000Feb 17 2000

    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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