Alwin Kienle’s research while affiliated with Ulm University and other places

In this study, we conducted optical goniometric measurements of turbid media and utilized a Monte Carlo model to characterize their optical properties. Our methodology employs an inverse fitting approach that combines Monte Carlo simulations with a Levenberg-Marquardt optimization routine. To enhance the accuracy and robustness of the inverse fitting process, we incorporated second-order derivatives into the algorithm. We also investigated the critical role of the single-scattering phase function and assessed the limitations associated with the commonly used Henyey-Greenstein (HG) approximation. To address these limitations, we developed a fitting procedure that determines the single-scattering phase function via cubic splines. This allows for the simultaneous extraction of both the cubic spline phase function and the scattering and absorption coefficients from goniometric measurements. This approach provides a refined and novel model for analyzing turbid media.

The most commonly used methods for the detection and identification of small microplastics generally require a complex sample preparation procedure and only allow for static measurements. Quality control of food and drinking water therefore requires a lot of effort. Especially in view of the increasing amount of plastic waste in the environment, the rising public awareness of the issue and the indications for adverse effects of microplastics on human health, more sophisticated measuring methods are required. In this paper, we present a measuring setup for the detection and identification of microplastics using flow Raman spectroscopy. We demonstrate the ability to acquire Raman spectra of individual particles as small as about 4 µm, enabling the identification of their plastic type. We show measurements of differently generated and shaped particles and particles made of different plastic types, highlighting the observed challenges and differences. Finally, we show possible applications of the measuring method. We demonstrate that the measuring principle is suitable for detecting and identifying microplastic particles among other particles and that aged plastics can still be distinguished by their Raman spectra. Overall, our results show that flow Raman spectroscopy is a promising method that could significantly reduce the effort required to detect microplastics.

Accurately recreating the appearance of translucent materials poses a substantial challenge in physically based rendering, primarily due to limitations in capturing complex light transport, especially subsurface scattering. Current methods often rely on approximations of radiative transfer theory and lack detailed characterization of material optical properties, limiting physical accuracy. Here, we introduce a comprehensive workflow that captures the full physical complexity needed to achieve true physical fidelity in translucent object rendering. Using a calibrated photo box environment, we validate our approach by comparing rendered scenes to their physical counterparts. Across a range of test materials, from silicon phantoms to everyday media like milk, our results show imperceptible color deviations, with ΔE values consistently below 1. By bridging the gap between real-world light behavior and digital simulations, our method enhances predictive rendering realism, paving the way for precise digital representations in medical and technical applications.

Accurately recreating the appearance of translucent materials poses a substantial challenge in physically based rendering, primarily due to limitations in capturing complex light transport, especially subsurface scattering. Current methods often rely on approximations of radiative transfer theory and lack detailed characterization of material optical properties, limiting physical accuracy. Here, we introduce a comprehensive workflow that captures the full physical complexity needed to achieve true physical fidelity in translucent object rendering. Using a calibrated photo box environment, we validate our approach by comparing rendered scenes to their physical counterparts. Across a range of test materials, from silicon phantoms to everyday media like milk, our results show imperceptible color deviations, with ΔE values consistently below 1. By bridging the gap between real-world light behavior and digital simulations, our method enhances predictive rendering realism, paving the way for precise digital representations in medical and technical applications.

This study investigates the relationship between the optical properties of articular cartilage with its biomechanical parameters. The absorption and reduced scattering coefficients of articular cartilage, and average maximum penetration depth, average maximum lateral spread, and average path length of photons were estimated by optical measurements and Monte Carlo simulation. The equilibrium and instantaneous moduli, and initial fibrillar network, strain-dependent fibrillar network, and nonfibrillar network moduli, and initial and deformation-dependent permeability of the tissue were estimated by multistep stress-relaxation measurement and fibril-reinforced poroelastic modeling. The relationship between the optical properties and biomechanical parameters was assessed using predictive regression modeling. A strong relationship was found between reduced scattering coefficients, averaged maximum penetration depth, averaged maximum lateral spread, and average path length of photons with equilibrium, instantaneous, and initial fibrillar network moduli. We attribute this relationship to the collagen fibers as the main contributor to the scattering and biomechanical properties of the tissue.

In this paper, we report on the measurement of the optical properties (absorption and scattering coefficients) of photoluminescent turbid media using a homemade integrating sphere setup equipped with a tunable monochromatic light source. The hemispherical reflectance and transmission data are analyzed with the radiative transfer equation using a Monte Carlo simulation-based lookup table to obtain the optical properties of the sample. The results are compared with the optical properties received from a classical integrating sphere setup equipped with a broadband white light source. The additional light of the photoluminescence generates artifacts within the optical properties, which are not present using a monochromatic light source. Additionally, a batch of samples with a broad range of scattering coefficients and dye concentrations were prepared and characterized with the aforementioned setup. The findings can help to generate a digital twin with the optical properties of the sample, which improves the physically based rendering and the design of, e.g., white-light LEDs. Dental restoration and photodynamic therapy also benefit from determination of the optical properties of photoluminescent turbid media.

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in terms of optimal transmission and detected forward scattering influenced by the scattering phase function are investigated. We built and validated a setup that measures the collimated transmission signal in a spectral range from 300 nm to 2150 nm. The system was validated using polystyrene spheres and Mie calculations. The limits of the system in terms of optimal transmission and detected forward scattering were researched. The optimal working parameters of the system, analyzed by simulations using the Monte Carlo method, show that the transmission should be larger than 10% and less than 90% to allow for a reliable measurement with acceptable errors caused by noise and systematic errors of the system. The optimal transmission range is between 25% and 50%. We show that the phase function is important when considering the accuracy of the measurement. For strongly forward-scattering samples, errors of up to 80% can be observed, even for a very small numerical aperture of 6.6·10−4, as used in our experimental system. We also show that errors increase with optical thickness as the ballistic transmission decreases and the multiscattered fraction increases. In addition, errors caused by multiple reflections in the sample layer were analyzed and also classified as relevant for classical absorption spectroscopy.

This work describes the development of silicone-based evaluation phantoms for biomedical optics in the wavelength range from 400 to 1550 nm. The absorption coefficient μa and the reduced scattering coefficient $\mu _\text {s}^{\prime }$ μ s ′ were determined using an integrating sphere setup. Zirconium dioxide pigments were used as scatterers and carbon black as absorbers. We developed an in-house manufacturing process using a Hauschild SpeedMixer to ensure reproducibility. A set of nine cubic phantoms with three different reduced scattering and absorption coefficients was produced. Prediction of the μa and $\mu _\text {s}^{\prime }$ μ s ′ was done by using the weighted mass concentrations of the used materials. The average prediction accuracy over all wavelengths and phantoms is 1.0% for the reduced scattering coefficient and 3.5% for the absorption coefficient.

Wavefront shaping is a well-known method of restoring a focus deep within scattering media by manipulating the incident light. However, the achievable focus enhancement depends on and is limited by the optical and geometrical properties of the medium. These properties contribute to the number of linearly independent transmission channels for light propagating through the turbid medium. Correlations occur when the number of incident waves coupled into the scattering medium exceeds this finite number of transmission channels. This paper investigates the wavefront shaping of such correlated electric fields. The influence of the observed correlations persists even though the average electric field distribution at positions in the focal plane follows a circular complex Gaussian. We show that correlations of the transmitted electric fields reduce the achievable intensity enhancement, even deep in the turbid medium. The investigations are carried out using a Monte Carlo algorithm. It is based on the speckle statistics of independent waves and introduces correlations of neighbouring electric fields via a Cholesky decomposition of the covariance matrix. Additional investigations include scenarios where the electric fields are not completely randomized, such as for ballistic or insufficiently scattered light. Significant contributions from such little-scattered light are observed to reduce the intensity enhancement further. Data from simulations solving Maxwell’s equations are compared with the results obtained from the Monte Carlo simulations for validation throughout this paper.

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models.