The estimation of optical properties of highly turbid and opaque biological tissue is a difficult task since conventional
purely optical methods rapidly loose sensitivity as the mean photon path length decreases. Photothermal
methods, such as pulsed or frequency domain photothermal radiometry (FD-PTR), on the other hand, show
remarkable sensitivity in experimental conditions that produce very feeble optical signals. Photothermal Radiometry
is primarily sensitive to absorption coefficient yielding considerably higher estimation errors on scattering
coefficients. Conversely, purely optical methods such as Local Diffuse Reflectance (LDR) depend mainly on
the scattering coefficient and yield much better estimates of this parameter. Therefore, at moderate transport
albedos, the combination of photothermal and reflectance methods can improve considerably the sensitivity of detection of tissue optical properties. The authors have recently proposed a novel method that combines FD-PTR with LDR, aimed at improving
sensitivity on the determination of both optical properties. Signal analysis was performed by global fitting the
experimental data to forward models based on Monte-Carlo simulations. Although this approach is accurate, the
associated computational burden often limits its use as a forward model. Therefore, the application of analytical
models based on the diffusion approximation offers a faster alternative. In this work, we propose the calculation
of the diffuse reflectance and the fluence rate profiles under the δ-P1 approximation. This approach is known
to approximate fluence rate expressions better close to collimated sources and boundaries than the standard
diffusion approximation (SDA). We extend this study to the calculation of the diffuse reflectance profiles. The
ability of the δ-P1 based model to provide good estimates of the absorption, scattering and anisotropy coefficients
is tested against Monte-Carlo simulations over a wide range of scattering to absorption ratios. Experimental
validation of the proposed method is accomplished by a set of measurements on solid absorbing and scattering
A novel method for measuring the optical properties of highly absorbing and scattering biological media is described. The method combines frequency-domain photothermal radiometry (FD-PTR) with spatially resolved diffuse reflectance (SR-DR) techniques aimed at improving sensitivity on the determination of both scattering
and absorption coefficients. Simulation results with Monte-Carlo and Diffusion Theory approaches that assess the scope and feasibility of the method are presented. An optical fiber probe for SR-DR measurements was constructed for operations at small source-detector separations and an FD-PTR system was adapted for quasi-simultaneous
operation with the probe. Several experiments on epoxy phantoms that illustrate the validity and potential of the method are presented.
Lidar receivers perform time and/or space averaging to decrease the variance of the optical power estimates. In this paper we study an Avalanche PhotoDiode based receiver. The number samples to reach a given minimum variance depends on the receiver transfer function. Herein, we review the linear receiver and derive the number of samples for the logarithmic pre-amplifier. Comparing the two receivers, we show that the signal variance for the logarithmic case is degraded by a factor that vanishes as the receiver aperture increases. These results can be readily applied to the problem of estimating log-power returns in the context of Differential Absorption lidar systems. As an application example, we study two different log-power estimators and compare their performance.
This paper addresses the joint estimation of backscatter and extinction coefficients from range/time noisy data under a nonlinear stochastic filtering setup. This problem is representative of many remote sensing applications such as weather radar and elastic-backscatter lidar. A Bayesian perspective is adopted. Thus, in addition to the observation mechanism, relating in a probabilistic sense the observed data with the parameters to be estimated, a prior probability density function has to be specified. We adopt as prior a causal first order auto-regressive Gauss-Markov random field. By using a reduced order state-space representation of the prior, we derive a nonlinear stochastic filter that recursively computes the backscatter and extinction coefficients at each site. A set of experiments based on simulated data illustrates the potential of the proposed approach.