Evolution-theory-based algorithm for optical diffusion tomography

Andreas H. Hielscher; Alexander D. Klose

doi:10.1117/12.407626

22 November 2000 Evolution-theory-based algorithm for optical diffusion tomography

Andreas H. Hielscher, Alexander D. Klose

Proceedings Volume 4160, Photon Migration, Diffuse Spectroscopy, and Optical Coherence Tomography: Imaging and Functional Assessment; (2000) https://doi.org/10.1117/12.407626
Event: EOS/SPIE European Biomedical Optics Week, 2000, Amsterdam, Netherlands

Abstract

In diffuse optical diffuse tomography (DOT) 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 (mu) _(alpha ), the scattering coefficient (mu) _s, or a combination thereof. Most of the currently employed imaging algorithms are model- based iterative image reconstruction (MOBIIR) schemes that employ information about the gradient of a suitably defined objective function with respect to the optical properties. In this approach the image reconstruction problem is considered as a nonlinear optimization problem, where the unknowns are the values of optical properties throughout the medium to be reconstructed. It is well known that gradient-based schemes are inefficient in areas where the gradient is close to zero. These schemes often get caught in local minima close to the starting point of the search and have problems finding the global minimum. To overcome this problem, we propose to employ optimization algorithms that make use of evolution strategies. These schemes are in general much better suited to find global minima and may be a better choice for the image reconstruction problem in diffuse optical tomography.

Citation Download Citation

Andreas H. Hielscher and Alexander D. Klose "Evolution-theory-based algorithm for optical diffusion tomography", Proc. SPIE 4160, Photon Migration, Diffuse Spectroscopy, and Optical Coherence Tomography: Imaging and Functional Assessment, (22 November 2000); https://doi.org/10.1117/12.407626

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KEYWORDS

Reconstruction algorithms

Optical properties

Image restoration

Sensors

Optical tomography

Inverse optics

Tomography

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