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To provide another modality for three-dimensional (3D) medical imaging, new techniques were developed to reconstruct a 3D structure in a turbid medium from a single blurred 2D image obtained using near-infrared transillumination imaging. One technique uses 1D information of a curvilinear absorber, or the intensity profile across the absorber image. Profiles in different conditions are calculated by convolution with the depth-dependent point spread function (PSF) of the transillumination image. In databanks, profiles are stored as lookup tables to connect the contrast and spread of the profile to the absorber depth. One-to-one correspondence from the contrast and spread to the absorber depth and thickness were newly found. Another technique uses 2D information of the transillumination image of a volumetric absorber. A blurred 2D image is deconvolved with the depth-dependent PSF, thereby producing many images with points of focus on different parts. The depth of the image part can be estimated by searching the deconvolved images for the image part in the best focus. To suppress difficulties of high-spatial-frequency noise, we applied a noise-robust focus stacking method. Experimentation verified the feasibility of the proposed techniques, and suggested their applicability to curvilinear and volumetric absorbers such as blood vessel networks and cancerous lesions in tissues.

The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.

We considered the higher-order TEM n,m modes in the laser illumination of a turbid medium. Then, we applied the Monte Carlo technique to the Hermite Gaussian beam as well as on the turbid medium. Hence, we extracted the optical properties like absorption and scattering by applying the stochastic variables on both the illumination and the turbid medium. The technique is based on equating the random variable to the cumulative density function (CDF) corresponding to the probability density functions. The accept/reject technique is used to get the CDF as outlined in the presented pseudo code.

The development of optical sensors, especially with regard to the improved resolution of cameras, has made optical techniques more applicable in medicine and live animal research. Research efforts focus on image signal acquisition, scattering de-blur for acquired images, and the development of image reconstruction algorithms. Rapidly evolving artificial intelligence has enabled the development of techniques for de-blurring and estimating the depth of light-absorbing structures in biological tissues. Although the feasibility of applying deep learning to overcome these problems has been demonstrated in previous studies, limitations still exist in terms of de-blurring capabilities on complex structures and the heterogeneity of turbid medium, as well as the limit of accurate estimation of the depth of absorptive structures in biological tissues (shallower than 15.0 mm). These problems are related to the absorption structure’s complexity, the biological tissue’s heterogeneity, the training data, and the neural network model itself. This study thoroughly explores how to generate training and testing datasets on different deep learning models to find the model with the best performance. The results of the de-blurred image show that the Attention Res-UNet model has the best de-blurring ability, with a correlation of more than 89% between the de-blurred image and the original structure image. This result comes from adding the Attention gate and the Residual block to the common U-net model structure. The results of the depth estimation show that the DenseNet169 model shows the ability to estimate depth with high accuracy beyond the limit of 20.0 mm. The results of this study once again confirm the feasibility of applying deep learning in transmission image processing to reconstruct clear images and obtain information on the absorbing structure inside biological tissue. This allows the development of subsequent transillumination imaging studies in biological tissues with greater heterogeneity and structural complexity.

We explore the propagation of structured vortex laser beams-shaped light carrying orbital angular momentum (OAM)-through complex multiple scattering medium. These structured vortex beams consist of a spin component, determined by the polarization of electromagnetic fields, and an orbital component, arising from their spatial structure. Although both spin and orbital angular momenta are conserved when shaped light propagates through a homogeneous, low-scattering medium, we investigate the conservation of these angular momenta during the propagation of Laguerre–Gaussian (LG) beams with varying topological charges through a turbid multiple scattering environment. Our findings demonstrate that the OAM of the LG beam is preserved, exhibiting a distinct phase shift indicative of the ‘twist of light’ through the turbid medium. This preservation of OAM within such environments is confirmed by in-house developed Monte Carlo simulations, showing strong agreement with experimental studies. Our results suggest exciting prospects for leveraging OAM in sensing applications, opening avenues for groundbreaking fundamental research and practical applications in optical communications and remote sensing.

In this paper, we report a high-speed electric field Monte Carlo simulation to calculate the transmission matrix of turbid medium. To accelerate the simulation speed, the Monte Carlo process is performed on a GPU. By the utilization of Mie scattering theory and random sample method in the simulation, the propagation of electric field in turbid medium can be precisely studied. Through the electric field Monte Carlo simulation, we derive the transmission matrix for a focal spot in the turbid medium. Based on this transmission matrix, the optical phase of input light is modulated to restrain the speckle noise and a high-quality focal spot can be reconstructed inside a turbid medium.

Topologically structured light carrying orbital angular momentum (OAM) has emerged as a powerful tool for nano-photonics and biomedical optics, yet conventional integer-charge Laguerre–Gaussian (LG) beams suffer from rotational degeneracy that limits diagnostic precision. Here, we demonstrate that conical refraction (CR) beams, specifically the Lloyd, Poggendorff, and Raman families, overcome this fundamental limitation through their inherent generation of fractional OAM states with unambiguous phase signatures. Through systematic interferometric comparison of LG ( = 3, 5) and CR beam propagation in tissues, we show that CR beams achieve superior diagnostic performance: while LG beams exhibit three-fold rotational ambiguity (4.19 rad uncertainty), Poggendorff CR beams provide phase determination with 0.08 rad precision. Both LG and CR beam families display remarkable topological resilience, preserving phase coherence as they traverse tissue samples while attaining refractive index sensitivity at the 10 level, three orders of magnitude beyond conventional refractometry. Most significantly, we present the first experimental evidence that CR beams can discriminate between healthy and cancerous kidney tissues through distinct phase rotations (4.71 vs. 5.04 rad, < 0.001) and a tenfold amplification in polarisation-induced distortion. The fractional topological charges of CR beams, ranging continuously between integer values, expand the accessible OAM phase space and enable 3.7-fold superior signal-to-noise ratio compared to measurements. These results establish CR-generated fractional OAM as the preferred modality for label-free tissue diagnostics, bridging fundamental nanophotonics with clinical applications in cancer detection and intraoperative margin assessment.

Optical polarimetry is a mighty tool for study of transparent and translucent inorganic and organic materials. Growing interest in better health and also the quality of the food pointed the investigation of physical properties of biological turbid tissues. Due to the fact that biological tissue is complex random material showing inhomogeneity, anisotropy and nonlinearity in the structure, its rigorous characterization is almost impossible. This complexity also involves an important amount of information. Therefore, the research of polarization states of scattered light is one of emerging novel techniques in biomedical science. The paper deals with the experimental study of degree of polarization and also with simulation of the biological tissue by Monte Carlo method.

Application possibility of quantitative ellipsometry method for studies of optical anisotropy and structural heterogeneity of the skin in vivo is shown. To describe the polarization properties of the depolarizing optically-active biotissue medium, the Mueller matrix algebra is used. Based on comparative analysis of the technical options and their application in experiments with biotissue, a setup for recording of the polarization state of the backscattered radiation was developed. It is proposed tо use the emitting channel of the LEF-3 ellipsometer in the optical scheme of the stand to have a uniform intensity distribution along the cross section of the input radiation beam, and also to form the polarization states necessary for the study. Radiation source wavelength selection in the spectral range (He-Ne laser, 632 nm) is justified, when scattering of radiation in turbid biological media predominates over absorption that makes it possible to estimate the structural parameters of the sample by the change of the output radiation polarization state. The receiving channel of the output polarization state analyzer was developed; it contains a video information block based on a color matrix sensor with a unified analysis field providing the possibility of further multispectral studying of the skin surface structure. The method of ellipsometric examination of the skin is proposed based on the distribution visualization of the polarization state parameters along the cross section of the output radiation beam and on its following analysis. An algorithm and software are developed with a Python language for image processing and calculation of the polarization characteristics of the sample. The distributions of the polarization

A view-based approach for the computation of updates of optical parameters of a turbid medium is discussed. The approach differs from conventionally employed reconstruction techniques in terms of implementation of the computed updates. Simulation studies in frequency domain for tissue phantoms approximated by slab geometry have been presented. Results of the study show that the proposed inversion scheme, wherein the projection data corresponding to each view has been handled individually, works well in predicting the presence of an inhomogeneity. A comparison with the reconstruction results of conventionally employed inversion schemes involving simultaneous handling of projection data from all the view angles shows that the accuracy of the proposed scheme in predicting the presence of single inhomogeneity is higher and the reconstruction is also relatively free of artifacts. On the other hand, in the presence of multiple inhomogeneities, though the simultaneous handling of all the views gives better reconstruction, the updates obtained by the proposed scheme can be employed as close a priori information about the approximate positions of the inhomogeneities, thereby reducing the overall dimension of the Jacobian matrix to be inverted and hence making the convergence faster.