Estimation Of Optical Parameters Research Articles

Multi-parameter estimation resistant to the random polarization effect for coherent optical receivers.

Optical parameter estimation based on the data obtained by coherent optical receivers is critical for optical performance monitoring (OPM) and the stable operation of the receiver digital signal processing (DSP). A robust multi-parameter estimation is intricate due to the interference of various system effects. By resorting to the cyclostationary theory, we are able to formulate a chromatic dispersion (CD), frequency offset (FO), and optical signal-to-noise ratio (OSNR) joint estimation strategy that is resistant to the random polarization effect, including polarization mode dispersion (PMD) and polarization rotation. The method uses data directly after the DSP resampling and matched filtering. Both numerical simulation and field optical cable experiment validate our method.

Estimating optical parameters of biological tissues with photon-counting micro-CT.

Wavelength-dependent absorption and scattering properties determine the fluorescence photon transport in biological tissues and image resolution of optical molecular tomography. Currently, these parameters are computed from optically measured data. For small animal imaging, estimation of optical parameters is a large-scale optimization problem, which is highly ill-posed. In this paper, we propose a new, to the best of our knowledge, approach to estimate optical parameters of biological tissues with photon-counting micro-computed tomography (micro-CT). From photon-counting x-ray data, multi-energy micro-CT images can be reconstructed to perform multi-organ segmentation and material decomposition in terms of tissue constituents. The concentration and characteristics of major tissue constituents can be utilized to calculate the optical absorption and scattering coefficients of the involved tissues. In our study, we perform numerical simulation, phantom experiments, and in vivo animal studies to calculate the optical parameters using our proposed approach. The results show that our approach can estimate optical parameters of tissues with a relative error of <10%, accurately mapping the optical parameter distributions in a small animal.

Improved silicon optical parameters at 25°C, 295 K and 300 K including temperature coefficients

AbstractAlthough the author's earlier tabulations of silicon's refractive index and absorption coefficient over a wide range of wavelengths have found wide use, recent luminescent measurements have shown that refinement is needed at wavelengths beyond 1200 nm. An updated dataset is described for lightly doped silicon tabulated over the 250‐ to 1450‐nm range in 10‐nm increments at three temperatures, 25°C, 295 K and 300 K, incorporating the most recent experimental results. Temperature coefficients are also included allowing reasonably accurate estimates of optical parameters over the much broader −24°C to 200°C range.

Synthesis, growth, optical and third order nonlinear optical properties of l-Phenylalanine d-Mandelic acid single crystal for photonic device applications

Organic nonlinear optical l-phenylalanine d-mandelic acid (LPDMA) single crystals have been harvested adopting slow solvent evaporation solution growth technique (SSEST) with growth period of 15 days. The XRD studies of the crystal confirm the non-centrosymmetric nature of the crystal. The presence of functional groups for the formation of the compound was confirmed by Fourier Transform Infrared (FTIR) analysis. The UV–Visible spectral studies ascertained linear optical quality and estimation of optical parameters. The nonlinear optical property was studied from Kurtz-Perry powder technique and Z-scan technique. The quantum chemical calculations such as HOMO–LUMO and Molecular Electrostatic Potential (MEP) were calculated and presented. In order to find out various mechanical parameters such as Vicker’s hardness number (Hv), Mayer’s index (n), yield strength (σy) and stiffness constant (C11), Vicker’s microhardness tester was utilized.

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography.

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

Quantitative photoacoustic tomography augmented with surface light measurements.

Quantitative photoacoustic tomography is an imaging modality in which distributions of optical parameters inside tissue are estimated from photoacoustic images. This optical parameter estimation is an ill-posed problem and it needs to be approached in the framework of inverse problems. In this work, utilising surface light measurements in quantitative photoacoustic tomography is studied. Estimation of absorption and scattering is formulated as a minimisation problem utilising both internal quantitative photoacoustic data and surface light data. The image reconstruction problem is studied with two-dimensional numerical simulations in various imaging situations using the diffusion approximation as the model for light propagation. The results show that quantitative photoacoustic tomography augmented with surface light data can improve both absorption and scattering estimates when compared to the conventional quantitative photoacoustic tomography.

Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

Optical parameters (properties) of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC) simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5[Formula: see text] angle to the phantoms. For the illumination of light, a laser light source at 633[Formula: see text]nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE) and evans blue (EB) dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs) in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

Efficient Inclusion of Total Variation Type Priors in Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating the distribution of optical parameters inside tissues from photoacoustic images, which are formed by combining optical information and ultrasonic propagation. This optical parameter estimation problem is ill-posed and needs to be approached within the framework of inverse problems. Photoacoustic images are three-dimensional and high-resolution. Furthermore, high-resolution reconstructions of the optical parameters are targeted. Therefore, in order to provide a practical method for quantitative photoacoustic tomography, the inversion algorithm needs to be able to perform successfully with problems of prominent size. In this work, an efficient approach for the inverse problem of quantitative photoacoustic tomography is proposed, assuming an edge-preferring prior for the optical parameters. The method is based on iteratively combining priorconditioned LSQR with a lagged diffusivity step and a linearization of the measurement model, with the needed multiplications by Jacobians performed in a matrix-free manner. The algorithm is tested with three-dimensional numerical simulations. The results show that the approach can be used to produce accurate and high quality estimates of absorption and diffusion in complex three-dimensional geometries with moderate computation time and cost.

Finite element modeling of light propagation in turbid media under illumination of a continuous-wave beam.

Spatially resolved spectroscopy provides a means for measuring the optical properties of biological tissues, based on analytical solutions to diffusion approximation for semi-infinite media under the normal illumination of an infinitely small light beam. The method is, however, prone to error in measurement because the actual boundary condition and light beam often deviate from that used in deriving the analytical solutions. It is therefore important to quantify the effect of different boundary conditions and light beams on spatially resolved diffuse reflectance in order to improve the measurement accuracy of the technique. This research was aimed at using finite element method (FEM) to model light propagation in turbid media, subjected to normal illumination by a continuous-wave beam of infinitely small or finite size. Three types of boundary conditions [i.e., partial current (PCBC), extrapolated (EBC), and zero (ZBC)] were evaluated and compared against Monte Carlo (MC) simulations, since MC could provide accurate fluence rate and diffuse reflectance. The effect of beam size was also investigated. Overall results showed that FEM provided results as accurate as those of the analytical method when an appropriate boundary condition was applied. ZBC did not give satisfactory results in most cases. FEM-PCBC yielded a better fluence rate at the boundary than did FEM-EBC, while they were almost identical in predicting diffuse reflectance. Results further showed that FEM coupled with EBC effectively simulated spatially resolved diffuse reflectance under the illumination of a finite size beam. A large beam introduced more error, especially within the region of illumination. Research also confirmed an earlier finding that a light beam of less than 1 mm diameter should be used for estimation of optical parameters. FEM is effective for modeling light propagation in biological tissues and can be used for improving the optical property measurement by the spatially resolved technique.

Diffuse reflectance spectroscopy for monitoring physiological and morphological changes in oral cancer

Oral cancer is prevalent in India due to addictive habits such as smoking and tobacco chewing. Simple steady-state diffuse reflectance spectroscopic technique may facilitate routine check up and early diagnosis of oral cancer. In this regard, inverse Monte Carlo algorithm is implemented to estimate the optical properties of tissue from diffuse reflectance spectra. A modified tissue model has been proposed in this algorithm which considers predominant Rayleigh scattering in the visible region and absorption of methemoglobin and melanin in tissue. The proposed tissue model improves the accuracy of estimation of optical properties. The root mean square variance between the measured and modeled diffuse reflectance spectra is found to be less than 5%. Further, lookup table based technique adopted in this algorithm enables real-time estimation of optical parameters of tissue in about 5s. Significant variations in the scattering coefficient, hemoglobin oxygen saturation and fraction of methemoglobin are identified between normal and cancer tissues. Step-wise linear discriminant analysis have revealed that the tissue parameters were statistically significant and the cancer tissue are discriminated from normal with 90% sensitivity and 90% specificity.

Quantitative photoacoustic tomography using illuminations from a single direction.

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating optical parameters inside tissues from photoacoustic images, which are formed by combining optical information and ultrasonic propagation. This optical parameter estimation problem is ill-posed and needs to be approached within the framework of inverse problems. It has been shown that, in general, estimating the spatial distribution of more than one optical parameter is a nonunique problem unless more than one illumination pattern is used. Generally, this is overcome by illuminating the target from various directions. However, in some cases, for example when thick samples are investigated, illuminating the target from different directions may not be possible. In this work, the use of spatially modulated illumination patterns at one side of the target is investigated with simulations. The results show that the spatially modulated illumination patterns from a single direction could be used to provide multiple illuminations for quantitative photoacoustic tomography. Furthermore, the results show that the approach can be used to distinguish absorption and scattering inclusions located near the surface of the target. However, when compared to a full multidirection illumination setup, the approach cannot be used to image as deep inside tissues.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.

Linear optical characterization of transparent thin films by the Z-scan technique

We report experimental characterization of a very small rectangular phase shift (<0.3 rad) obtained from the far-field diffraction patterns using a closed aperture Z-scan technique. The numerical simulations as well as the experimental results reveal a peak-valley configuration in the far-field normalized transmittance, allowing us to determine the sign of the dephasing. The conditions necessary to obtain useful Z-scan traces are discussed. We provide simple linear expressions relating the measured signal to the phase shift. A very good agreement between calculated and experimental Z-scan profiles validates our approach. We show that a very well known nonlinear characterization technique can be extended for linear optical parameter estimation (as refractive index or thickness).

Estimates of Optical Parameters Based on Mixing Rules Extending over a Wide Range of Wavelengths

We examine a procedure for constructing estimates of optical parameters for composite materials, whose associated reflectivity function consists of multiscale features relative to wavelength, via mixing rules that extend over a wide range of wavelengths. The parameters associated with these mixing rules define a system representation of the optical properties of a composite material. In our procedure, these parameters are optimized with respect to the measured reflectivity of the composite material and existing optical data for materials similar to those making up the composite system. Estimates of optical parameters that extend over a wide range of wavelengths can provide for well-conditioned inversion, via parameter optimization, of reflectivity measurements characterized by multiscale features relative to wavelength.

Estimation of optical parameters in a living tissue by solving the inverse problem of the multiflux radiative transfer

Calculations of radiative transfer require knowledge of the absorption and scattering coefficients and the asymmetry factor of scattering in the medium. A method is presented for estimating these coefficients in living plant leaves from fiber-optic measurements. We consider the plant leaf as consisting of two layers of different refractive indices and with reflecting surfaces. Light intensities at the boundaries of these layers in several irradiated plant leaves have been measured using a thin (70-microm) glass fiber connected to a photomultiplier. The diffuse reflection and transmission were measured with an integrating sphere. From these values we derive an estimation of the scattering and absorption coefficients and the asymmetry factor of scattering applying an inversion of the multiflux theory of light propagation in turbid media. In addition, we compare these coefficients with those obtained by using the Kubelka-Munk theory.

Optimizing null ellipsometry for oxidized silicon

Polarizer imperfections which produce a deviation of the measuring light beam cause systematic errors in the estimation of optical parameters particularly for low absorbing systems such as oxidized silicon. These can be reduced considerably by using the polarizer, specimen, compensator, analyzer (PSCA) instead of the PCSA arrangement. In addition, PSCA measurements can be made nearer to the principal angle where sensitivity is greatest. The discrepancy of Delta measurements between zones is reduced by a factor of 20. Precision is improved by a factor of 10-50 for the PSCA measurements near the principal angle compared with measurements at any angle of incidence for the PCSA arrangement. Results on silicon covered with a natural oxide are compared for illustration.

Joint probability of instants of occurrence of photon events and estimation of optical parameters

Using a Karhunen-Loeve modal expansion of a stationary and gaussian optical field an expression is derived for the joint probability of instants of occurrence of photon events. When the observation time is much longer than the coherence time, this expression takes a simple form. In this case, and for lorentzian light, it is shown that measurement of the total number of counts is sufficient to determine the maximum likelihood estimate (MLE) of the average count rate. A nonlinear equation is found whose solution gives the MLE of the bandwidth.