David Hevisov

The blue appearance of veins has been addressed in previous studies; however, an experimental demonstration that reproduces this phenomenon with comprehensible optical properties has not yet been achieved. To address this gap, silicone-based skin phantoms were fabricated to replicate the optical properties of human skin. Cylindrical vessel inclusions of varying depths and diameters were incorporated into the phantoms and filled with silicone mimicking blood at different oxygenation states. The vessels’ appearance was quantitatively evaluated using calibrated photography and hyperspectral imaging. Monte Carlo light transport simulations were employed to support the experimental findings. The phantoms with venous inclusions exhibited the characteristic blue hue of veins, thus demonstrating that vein blueness does not arise solely from blood’s intrinsic absorption but from the complex interplay of tissue optics and visual perception. These experimental findings support our previously proposed theory.

In the original article ( Opt. Express 33 , 15187 ( 2025 ) 10.1364/OE.553642 ) [1], the discussion of certain related works was incomplete. Including these references provides additional context and places the topics examined in a broader perspective.

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.

Optical diagnostic techniques represent an attractive complement to conventional pulp vitality tests, as they can provide direct information about the vascular status of the pulp. However, the complex, multi-layered structure of a tooth significantly influences the detected signal and, ultimately, the diagnostic decision. Despite this, the impact of the various dental components on light propagation within the tooth, particularly in the context of diagnostic applications, remains insufficiently studied. To help bridge this gap and potentially enhance diagnostic accuracy, this study employs digital optical twins based on the Monte Carlo method. Using incisor and molar models as examples, the influence of tooth and pulp geometry, blood concentration, and pulp composition, such as the possible presence of pus, on spectrally resolved transmission signals is demonstrated. Furthermore, it is shown that gingival blood absorption can significantly overlay the pulpal measurement signal, posing a substantial risk of misdiagnosis. Strategies such as shifting the illumination and detection axes, as well as time-gated detection, are explored as potential approaches to suppress interfering signals, particularly those originating from the gingiva.

Optical diagnostic techniques represent an attractive complement to conventional pulp vitality tests, as they can provide direct information about the vascular status of the pulp. However, the complex, multi-layered structure of a tooth significantly influences the detected signal and, ultimately, the diagnostic decision. Despite this, the impact of the various dental components on light propagation within the tooth, particularly in the context of diagnostic applications, remains insufficiently studied. To help bridge this gap and potentially enhance diagnostic accuracy, this study employs digital optical twins based on the Monte Carlo method. Using incisor and molar models as examples, the influence of tooth and pulp geometry, blood concentration, and pulp composition, such as the possible presence of pus, on spectrally resolved transmission signals is demonstrated. Furthermore, it is shown that gingival blood absorption can significantly overlay the pulpal measurement signal, posing a substantial risk of misdiagnosis. Strategies such as shifting the illumination and detection axes, as well as time-gated detection, are explored as potential approaches to suppress interfering signals, particularly those originating from the gingiva.

The increasing use of colored materials in 3D printing enables the creation of complex structures with realistic color appearances. Ensuring that the printed colors match the design requires advanced rendering methods to accurately predict the final product. We use a fully optical property-based pipeline for rendering the physically correct color of 3D-printed objects, achieving results nearly indistinguishable compared to the final printed samples. Our approach is based entirely on the intrinsic optical properties of the printing materials: the reduced scattering coefficient, absorption coefficient, refractive index, and scattering anisotropy factor. First, we employed an integrating sphere setup to measure the spectrally resolved absorption and reduced scattering coefficients of the base materials. This method was extended to handle both highly scattering and nearly non-scattering materials with precision. Next, a spectral Monte Carlo path tracing simulation was used to compute light transfer through the printed object, fully modeling the physics of light propagation in heterogeneous turbid media. Our results achieved CIE Δ E ₂₀₀₀ values below 2.0, with renderings accurately reproducing the colors of the specially designed validation samples printed using a Stratasys PolyJet system. This pipeline provides a robust tool for predicting and optimizing the color appearance of 3D-printed objects. It offers broad applicability across various printing systems and material compositions while eliminating the need for time- and cost-intensive trial-and-error test prints.