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Experimental imaging and Monte Carlo modeling of ultrafast pulse propagation in thin scattering slabs.
Pattelli, Lorenzo; Mazzamuto, Giacomo.
Afiliação
  • Pattelli L; Istituto Nazionale di Ricerca Metrologica (INRiM), Torino, Italy.
  • Mazzamuto G; European Laboratory for Nonlinear Spectroscopy (LENS), Sesto Fiorentino, Italy.
J Biomed Opt ; 27(8)2022 06.
Article em En | MEDLINE | ID: mdl-35655345
ABSTRACT

SIGNIFICANCE:

Most radiative transport problems in turbid media are typically associated with mm or cm scales, leading to typical time scales in the range of hundreds of ps or more. In certain cases, however, much thinner layers can also be relevant, which can dramatically alter the overall transport properties of a scattering medium. Studying scattering in these thin layers requires ultrafast detection techniques and adaptations to the common Monte Carlo (MC) approach.

AIM:

We aim to discuss a few relevant aspects for the simulation of light transport in thin scattering membranes, and compare the obtained numerical results with experimental measurements based on an all-optical gating technique.

APPROACH:

A thin membrane with controlled scattering properties based on polymer-dispersed TiO2 nanoparticles is fabricated for experimental validation. Transmittance measurements are compared against a custom open-source MC implementation including specific pulse profiles for tightly focused femtosecond laser pulses.

RESULTS:

Experimental transmittance data of ultrafast pulses through a thin scattering sample are compared with MC simulations in the spatiotemporal domain to retrieve its scattering properties. The results show good agreement also at short distances and time scales.

CONCLUSIONS:

When simulating light transport in scattering membranes with thicknesses in the orders of tens of micrometer, care has to be taken when describing the temporal, spatial, and divergence profiles of the source term, as well as the possible truncation of step length distributions, which could be introduced by simple strategies for the generation of random exponentially distributed variables.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diagnóstico por Imagem / Nanopartículas Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diagnóstico por Imagem / Nanopartículas Idioma: En Ano de publicação: 2022 Tipo de documento: Article