Abstract
The reconstruction of optical properties for opaque mediums is highly desired for medical, atmosphere and aerosol applications. However, the modeling and reconstruction process is highly related with multiple scattering phenomena, which elevates both the complexity and computational costs for such efforts. This work introduces a time-based Markov chain method, which uses a sparse transition matrix to represent the likelihood for a photon to transit in the turbid media. The accuracy of the time-based Markov chain model was verified against the forwarding calculations of the scattering-based Markov chain model and Monte Carlo simulations. Then, reconstruction was performed with backscattered photon angular distributions. Based on the characteristics of the sparse transition matrix, the optical properties (droplet diameters) could be obtained layer by layer with transmitted photon distributions at different time durations. It is shown that the time-based Markov chain model can reconstruct the optical properties of a turbid slab with satisfactory accuracy and lower computational costs.
Highlights
There is an increasing need for interpreting light scattering signals from turbid media, since scattering signals can contain crucial information about the optical characteristics of the media
In order to find temporal resolved angular distribution using the Markov chain model, the time or path length must be set as the event so that this information can be preserved as the photons are transmitted
We compare the performance of the time-based Markov chain model against the Monte Carlo simulation model
Summary
There is an increasing need for interpreting light scattering signals from turbid media, since scattering signals can contain crucial information about the optical characteristics of the media. The authors proposed a Markov chain-based model [15,16,17,18] to calculate the transmitted angular distribution through turbid slabs. In this model, multiple scattering was converted into a matrix (transition matrix), which represented the likelihood of a photon to transit from one state (location and propagation angle) to another. In order to find temporal resolved angular distribution using the Markov chain model, the time or path length must be set as the event so that this information can be preserved as the photons are transmitted.
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