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Updated: Jul 10, 2026
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Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
Published on: August 22, 2018
Reconstructing a thin absorbing obstacle in a half-space of tissue
Pedro González-Rodríguez1, Arnold D Kim, Miguel Moscoso
1School of Natural Sciences, PO Box 2039, University of California, Merced, Merced, California 95344, USA.
Summary
We present a novel method to locate and map absorbing obstacles within a scattering medium using plane-wave illumination. This technique accurately reconstructs obstacle depth and shape, even with noisy data.
Area of Science:
- Optics and Photonics
- Wave Phenomena
- Inverse Problems
Background:
- Solving direct and inverse obstacle-scattering problems is crucial in various scientific fields.
- Scattering media often exhibit forward-peaked scattering, necessitating specialized approximations.
- Thin, absorbing inhomogeneities pose unique challenges in detection and characterization.
Purpose of the Study:
- To develop a method for recovering the depth and shape of thin, absorbing obstacles in a scattering half-space.
- To utilize the modified Fokker-Planck approximation for modeling forward-peaked scattering.
- To enable obstacle reconstruction using plane-wave illumination and standard deconvolution techniques.
Main Methods:
- Applied the modified Fokker-Planck approximation to the radiative transport equation.
- Employed the first Born approximation to derive the obstacle recovery method.
- Utilized nonlinear least-squares for depth recovery and deconvolution for shape reconstruction.
Main Results:
- Successfully recovered the depth of the absorbing obstacle.
- Reconstructed the shape of the obstacle using a derived point-spread function.
- Demonstrated the method's robustness against measurement noise through numerical simulations.
Conclusions:
- The developed method effectively solves direct and inverse obstacle-scattering problems in absorbing and scattering media.
- Accurate depth and shape reconstruction of thin, absorbing obstacles is achievable.
- The technique shows promise for practical applications even with noisy experimental data.
