Hyperspectral imaging (HSI) is a promising optical modality that is already being used in numerous applications. Further expansion of the capabilities of HSI depends on the modularity and versatility of the systems, which would, inter alia, incorporate profilometry, fluorescence imaging, and Raman spectroscopy while following a rigorous calibration and verification protocols, thus offering new insights into the studied samples as well as verifiable, quantitative measurement results applicable to the development of quantitative metrics. Considering these objectives, we developed a custom-made laboratory HSI system geared toward biomedical applications. In this report, we describe the design, along with calibration, characterization, and verification protocols needed to establish such systems, with the overall goal of standardization. As an additional novelty, our HSI system uses a custom-built broadband LED-based light source for reflectance imaging, which is particularly important for biomedical applications due to the elimination of sample heating. Three examples illustrating the utility and advantages of the integrated system in biomedical applications are shown. Our attempt presents both the development of a custom-based laboratory HSI system with novel LED light source as well as a framework which may improve technological standards in HSI system design.

In this work, we revise the preparation procedure and conduct an in depth characterization of optical properties for the recently proposed silicone-based tissue-mimicking optical phantoms in the spectral range from 475 to 925 nm. The optical properties are characterized in terms of refractive index and its temperature dependence, absorption and reduced scattering coefficients and scattering phase function related quantifiers. The scattering phase function and related quantifiers of the optical phantoms are first assessed within the framework of the Mie theory by using the measured refractive index of SiliGlass and size distribution of the hollow silica spherical particles that serve as scatterers. A set of purely absorbing optical phantoms in cuvettes is used to evaluate the linearity of the absorption coefficient with respect to the concentration of black pigment that serves as the absorber. Finally, the optical properties in terms of the absorption and reduced scattering coefficients and the subdiffusive scattering phase function quantifier γ are estimated for a subset of phantoms from spatially resolved reflectance using deep learning aided inverse models. To this end, an optical fiber probe with six linearly arranged optical fibers is used to collect the backscattered light at small and large distances from the source fiber. The underlying light propagation modeling is based on the stochastic Monte Carlo method that accounts for all the details of the optical fiber probe.

Optical imaging systems use a light source that illuminates a sample and a photodetector that detects light reflected from or transmitted through the sample. The sample surface curvature, surface-to-camera distance, and illumination-source-to-surface distance significantly affect the measured signal, resulting in image artifacts. To correct the images, a three-dimensional (3D) profilometry system was used to obtain 3D surface information. The 3D information enables image correction using Lambert cosine law and height correction. In this study, the feasibility of the correction method for push-broom hyperspectral imaging of three different objects is presented. Results show a significant reduction of image artifacts, making further image analysis more accurate and robust. The presented 3D profilometry method is applicable to all push-broom imaging systems and the described correction procedure can be applied to all spectral or monochromatic imaging systems.

Optical imaging techniques have shown great potential in medical applications, primarily in disease diagnosis and surgical guidance.¹ When light enters a tissue, it is affected by scattering or absorption in tissue chromophores, such as water, lipid, deoxyhemoglobin, oxyhemoglobin, and melanin.² In diseased tissues, their absorption and scattering properties change, resulting in altered diffuse reflectance spectra compared with healthy tissue.³ Diffuse reflectance spectra can be measured by optical techniques, such as hyperspectral imaging (HSI),⁴ and quantitative information about tissue composition can be extracted from the measured images.

Hyperspectral microscope imaging (HMI) is an emerging modality that integrates spatial information collected by standard laboratory microscopy and the spectral-based contrast obtained by hyperspectral imaging and may be instrumental in establishing novel quantitative diagnostic methodologies, particularly in histopathology. Further expansion of HMI capabilities hinges upon the modularity and versatility of systems and their proper standardization. In this report, we describe the design, calibration, characterization, and validation of the custom-made laboratory HMI system based on a Zeiss Axiotron fully motorized microscope and a custom-developed Czerny-Turner-type monochromator. For these important steps, we rely on a previously designed calibration protocol. Validation of the system demonstrates a performance comparable to classic spectrometry laboratory systems. We further demonstrate validation against a laboratory hyperspectral imaging system for macroscopic samples, enabling future comparison of spectral imaging results across length scales. An example of the utility of our custom-made HMI system on a standard hematoxylin and eosin-stained histology slide is also shown.

Analysis of morphological changes of the peritoneal membrane is an essential part of animal studies when investigating molecular mechanisms involved in the development of peritoneal fibrosis or testing the effects of potential therapeutic agents. Current methods, such as histology and immunohistochemistry, require time consuming sample processing and analysis and result in limited spatial information. In this paper we present a new method to evaluate structural and chemical changes in an animal model of peritoneal fibrosis that is based on hyperspectral imaging and a model of light transport. The method is able to distinguish between healthy and diseased subjects based on morphological as well as physiological parameters such as blood and scattering parameters. Furthermore, it enables evaluation of changes, such as degree of inflammation and fibrosis, that are closely related to histological findings.