This PDF file contains the front matter associated with SPIE Proceedings Volume 12827, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

We present a small footprint and affordable implementation of a multifocal image scanning microscope (ISM), utilizing both a microelectromechanical system (MEMS) micromirror for flexible optical excitation control, and multifocal patterned illumination using a custom 3D printed optical quality lenslet array. We highlight the individual element performance and demonstrate its use in fluorescence imaging to allow comparison of the affordable and customizable approach with its commercial counterpart.

In this study, we present an integrated stereoscopic and hyperspectral imaging system designed to overcome the limitations of traditional quantitative hyperspectral imaging, notably the dependency on precise camera-sample distance measurements. Our approach combines advanced depth-sensing technology with a compact hyperspectral camera, featuring integrated RGB sensors, to facilitate automated synchronization, system integration, and reconstruction through epipolar geometry and image co-registration. The system acquires hyperspectral data cubes along predefined camera trajectories, enabling full 3D hyperspectral representations via global alignment, a significant enhancement over conventional methods that lack depth resolution. This methodology has the potential to eliminate the need for strict camera-sample distance calibration and appends a morphological dimension to hyperspectral tissue analysis. The system's efficacy is demonstrated in vivo, focusing on non-contact human skin imaging. The integration of stereoscopic depth and hyperspectral data in our system marks a significant advancement in spectroscopic tissue analysis, with promising applications in telehealth, enhancing both the diagnostic capabilities and accessibility of advanced imaging technologies.

Hyperspectral imaging can capture light reflected from tissue with high spectral and spatial resolution. Fitting algorithms can be applied to the spectrum at each pixel to estimate tissue chromophore concentrations, including blood, melanin, water, and fat. Traditional fitting methods are computationally intensive and slow when applied over an entire image. This study developed an artificial neural network (ANN) to rapidly calculate tissue oxygenation, blood, and melanin content from hyperspectral images. Linearly polarized light from a halogen lamp was delivered through a ring illuminator placed 20 cm from the tissue surface. A 1024x1224 pixel hyperspectral camera captured diffusely reflected light through an orthogonal polarizer at 299 wavelengths between 400-1000nm. To train an ANN, diffusion theory was used to generate reflectance spectra from 440-800nm for a uniform tissue containing 24,000 random combinations of physiologically relevant concentrations of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering. The ANN was then tested by generating another 6,000 reflectance spectra from diffusion theory using physiological values and comparing the chromophore concentrations output by the ANN to ground truth values. The ANN demonstrated a root-mean-square error less than 0.01 in predicting each chromophore concentration from reflectance spectra simulated by diffusion theory. An in vivo finger occlusion experiment demonstrated the ability of the system to quantify changes in oxygen saturation and blood volume. This work demonstrates a new deep learning approach to rapidly process hyperspectral image data and accurately quantify tissue components.

We developed an approach to quantify intra-tumoral metabolic heterogeneity of in vivo tumor models by leveraging a computationally designed multi-scale microscope and a suite of exogenous fluorescent contrast agents to provide functional and structural information.

Paraformaldehyde (PFA) is one of the most common fixatives in biological and biomedical research. It is used to preserve tissue or cell morphology while preventing contamination by crosslinking proteins and other biological molecules. Although fixation is required for histology, it has been documented that chemical fixation can cause alterations in the fluorescence properties of exogenous and endogenous fluorophores, which are valuable markers for understanding biological processes, ultimately reducing the accuracy and reliability of quantitative fluorescence measurements. Therefore, there is a need for understanding the behavior of tissue fluorescence during PFA fixation. Multispectral fluorescence imaging (MFSI) is an imaging technique used in biological and biomedical research to visualize and quantify the fluorescence properties of tissue over several wavelength bands, enabling measurement of several fluorophores simultaneously.

To evaluate the effects of PFA on tissue fluorescence, we imaged brain tissue samples using MSFI from two cohorts of mice: the SOX10 Cre; R26R-Brainbow 2.1/Confetti mice (expressing four exogenous fluorophores), and wild type Cre-negative controls. Specimens from each were immersed in 10 ml of PFA or phosphate buffer saline (PBS) as a control. The fluorescence intensity was captured using MFSI every 15 minutes over three hours. Analysis was performed on the resulting images to produce quantitative metrics of the resulting fluorescence signal. The results show that exogenous fluorophores are dramatically quenched within the first half hour when fixed in PFA, whereas endogenous fluorescence increased slightly in the same time period. These results are valuable to understand how fixation can influence fluorescence properties and can inform optimal fixation protocols.

We explore the use of Monte-Carlo based optical modeling to predict light propagation, sensitivity, and optical interrogation volume (photon hitting density) of a miniature tissue-implantable optical sensor system. There are a limited number of Monte Carlo tools available that allow for the direct import of 3D models of complex optoelectronic systems. We therefore investigate the use of TracePro, a commercial Monte Carlo-based ray-tracing software package, to guide the design of a needle-injectable optical sensor designed for tumor monitoring. We first validated the use of TracePro to model light propagation in multiple scattering tissue by modeling simple infinite geometry systems and comparing light propagation to the known analytical diffusion approximation solutions. We also analyzed ray-tracing history (i.e., photon paths) and observed agreement to analytical models of the optical interrogation volume, varying by an average of 12% across source-detector separations ranging from 5 to 15mm. Finally, we describe how this approach was used to analyze and guide the design of the implantable optical sensor for tumor monitoring. Overall, TracePro provides a straightforward, easy-to-use, and accurate approach to importing and analyzing complex diffuse optical sensing systems.

Hyperspectral imaging (HSI) is a label-free imaging modality that is emerging for non-invasive detection of various diseases including cancers. HSI provides high-resolution spatial images where each pixel has a spectral curve with numerous wavelength bands from the visible to infrared ranges. The rich spatial and spectral information can be used to discriminate various types of tissues and pathophysiological conditions. However, it can be difficult to explain spectral data with respect to the underline cellular and molecular mechanism. In this study, we developed an approach that registers hyperspectral images and mass spectrometry (MS) data where MS provides tissue molecular profiles. Human prostate tissues that were obtained after prostatectomy were used in the experiments. The whole prostate was first sliced every six mm. A customized hyperspectral surgical microscope was used to acquire HSI data from the sliced tissue. For MS data analysis, the sliced tissue of the prostate was divided into 51 small regions and then processed separately for each region. The immediately adjacent tissue was sliced and processed histologically for H&E staining. The MS molecular profiles were correlated with the hyperspectral images in this study.