Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Diffusion approximation and its similarity transforms for estimating laser light absorption in tissue

Open Access Open Access

Abstract

The accuracy of the diffusion approximation was examined by comparing it with more accurate solutions of radiative transfer theory for 1-D geometry. The forward and backward fluxes computed with the diffusion approximation are appropriate for both high and isotropic scattering cases. However, there are errors associated with the fluence rate. Lower fluence rates are computed in the subsurface region of a slab of tissue even though fluxes are estimated accurately. Radiance patterns are highly anisotropic at the boundary or near the source, and the diffusion mode is not proper in this region. As absorption and anisotropy of scattering increase, deviations increase. For high absorption, errors are smaller in terms of the total fluence rate since the diffuse fluence rate is much smaller than the collimated fluence rate. For highly forward scattering, negative reflectance and fluence rates may be computed. For anisotropic scattering, transforms of optical coefficients using similarity relations and the delta-Eddington approximation improve accuracy. Estimations of reflection and transmission are much improved, especially with the delta-Eddington approximation for the Henyey-Greenstein phase function.

© 1988 Optical Society of America

PDF Article
More Like This
Utilizing Fokker-Planck-Eddington approximation in modeling light transport in tissues-like media

Ossi Lehtikangas and Tanja Tarvainen
879908 European Conference on Biomedical Optics (ECBO) 2013

Discrete-Ordinate Transport Simulations of Light Propagation in Highly Forward Scattering Heterogeneous Media

Andreas H. Hielscher and Raymond E. Alcouffe
ATuC2 Advances in Optical Imaging and Photon Migration (BIOMED) 1998

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.