Forward–adjoint fluorescence model: Monte Carlo integration and experimental validation

Abstract

The adjoint form of the photon transport equation is applied to a generalized fluorescence detection problem, and its accuracy is empirically tested. This approach can be interpreted as mathematically reversing the temporal flow of fluorescent photons; that is, they are tracked from the detector back to potential sites of origin in the scattering medium. The result is a distribution of potential fluorescing sites that, when properly normalized, gives a probability field of the relative importance of the photon starting position and direction to the resulting signal. This adjoint solution can be combined with the temporally forward-derived distribution of absorbed excitation photons to evaluate the fluorescence excitation detection scheme. This bypasses the normal, temporal derivation wherein the fluorescence transport solution is dependent on the result of the excitation transport solution.

Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation

“…The current MC model neglects all polarization effects that might result in anisotropic fluorescence emission [7]. Therefore, the fluorescence photons are emitted isotropically from the source points, this agrees with the assumptions proposed in [16][17][18][19][20][21][22][23]. Figure 4.…”

FluorMODleaf: A new leaf fluorescence emission model based on the PROSPECT model

“…In most media, the measured emission spectrum is distorted due to light reabsorption and/or scattering, which makes its interpretation especially difficult. Different theories have emerged that attempt to explain the propagation of light in a turbid medium, including fluorescence emission: radiative transfer equation (Richards-Kortum et al, 1989;Gardner et al, 1996;Emmel & Hersch, 1998), two-flux Kubelka-Munk (KM) theory and four-flux extensions (Allen, 1964;Fukshansky & Kazarinova, 1980;Bonham, 1986;Shakespeare & Shakespeare, 2003), Monte Carlo simulations (Wu et al, 1993;Crilly et al, 1997;Welch et al, 1997), or rigorous analysis of tissue fluorescence based on electromagnetic theory (Panou-Diamandi et al, 1998).…”

Accelerated Monte Carlo models to simulate fluorescence spectra from layered tissues

“…Except for a scaling factor, the emission probability E(r, t, z) is then given by the absorption probability A rev (r, t, z), obtained from a single Monte Carlo simulation based on the optical properties for the emission wavelength. This approach was followed by Crilly et al 22 for steady-state fluorescence, but without a derivation of the scaling factor coupling the forward and reverse computations. Instead, they relied on empirical determination by comparing with results generated by forward computations.…”

“…The aim of this study is to develop efficient fluorescence Monte Carlo models, thereby making the Monte Carlo technique more feasible for applications within fluorescence spectroscopy of turbid media such as tissues. We consider not only steady-state fluorescence 22,23 but also the dimension of time, enabling simulations of timeresolved fluorescence. Furthermore, the advantages and the limitations of the accelerated models are evaluated by rigorous comparison with the well-tested conventional Monte Carlo approach in terms of accuracy and computation time required to achieve a predefined signal-to-noise ratio.…”