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Abstract Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians. 

Colorectal cancer is the 3^rdleading cancer for incidence and mortality rates. Positive treatment outcomes have been associated with early detection; however, early stage lesions have limited contrast to surrounding mucosa. A potential technology to enhance early stagise detection is hyperspectral imaging (HSI). While HSI technologies have been previously utilized to detect colorectal cancerex vivoor post-operation, they have been difficult to employ in real-time endoscopy scenarios. Here, we describe an LED-based multifurcated light guide and spectral light source that can provide illumination for spectral imaging at frame rates necessary for video-rate endoscopy. We also present an updated light source optical ray-tracing model that resulted in further optimization and provided a ∼10X light transmission increase compared to the initial prototype. Future work will iterate simulation and benchtop testing of the hyperspectral endoscopic system to achieve the goal of video-rate spectral endoscopy. 

Positive outcomes for colorectal cancer treatment have been linked to early detection. The difficulty in detecting early lesions is the limited contrast with surrounding mucosa and minimal definitive markers to distinguish between hyperplastic and carcinoma lesions. Colorectal cancer is the 3rd leading cancer for incidence and mortality rates which is potentially linked to missed early lesions which allow for increased growth and metastatic potential. One potential technology for early-stage lesion detection is hyperspectral imaging. Traditionally, hyperspectral imaging uses reflectance spectroscopic data to provide a component analysis, per pixel, of an image in fields such as remote sensing, agriculture, food processing and archaeology. This work aims to acquire higher signal-to-noise fluorescence spectroscopic data, harnessing the autofluorescence of tissue, adding a hyperspectral contrast to colorectal cancer detection while maintaining spatial resolution at video-rate speeds. We have previously designed a multi-furcated LED-based spectral light source to prove this concept. Our results demonstrated that the technique is feasible, but the initial prototype has a high light transmission loss (~98%) minimizing spatial resolution and slowing video acquisition. Here, we present updated results in developing an optical ray-tracing model of light source geometries to maximize irradiance throughput for excitation-scanning hyperspectral imaging. Results show combining solid light guide branches have a compounding light loss effect, however, there is potential to minimize light loss through the use of optical claddings. This simulation data will provide the necessary metrics to verify and validate future physical optical components within the hyperspectral endoscopic system for detecting colorectal cancer. 

Hyperspectral imaging (HSI) technology has been applied in a range of fields for target detection and mixture analysis. While its original applications were in remote sensing, modern uses include agriculture, historical document authentications and medicine. HSI has shown great utility in fluorescence microscopy; however, acquisition speeds have been slow due to light losses associated with spectral filtering. We are currently developing a rapid hyperspectral imaging platform for 5-dimensional imaging (RHIP-5D), a confocal imaging system that will allow users to obtain simultaneous measurements of many fluorescent labels. We have previously reported on optical modeling performance of the system. This previous model investigated geometrical capability of designing a multifaceted mirror imaging system as an initial approach to sample light at many wavelengths. The design utilized light-emitting diodes (LEDs) and a multifaceted mirror array to combine light sources into a liquid light guide (LLG). The computational model was constructed using Monte Carlo optical ray software (TracePro, Lambda Research Corp.). Recent results presented here show transmission has increased up to 9% through parametric optimization of each component. Future work will involve system validation using a prototype engineered based on our optimized model. System requirements will be evaluated to determine if potential design changes are needed to improve the system. We will report on spectral resolution to demonstrate feasibility of the RHIP-5D as a promising solution for overcoming current HSI acquisition speed and sensitivity limitations. 

A major benefit of fluorescence microscopy is the now plentiful selection of fluorescent markers. These labels can be chosen to serve complementary functions, such as tracking labeled subcellular molecules near demarcated organelles. However, with the standard 3 or 4 emission channels, multiple label detection is restricted to segregated regions of the electromagnetic spectrum, as in RGB coloring. Hyperspectral imaging allows the user to discern many fluorescence labels by their unique spectral properties, provided there is significant differentiation of their emission spectra. The cost of this technique is often an increase in gain or exposure time to accommodate the signal reduction from separating the signal into many discrete excitation or emission channels. Recent advances in hyperspectral imaging have allowed the acquisition of more signal in a shorter time period by scanning the excitation spectra of fluorophores. Here, we explore the selection of optimal channels for both significant signal separation and sufficient signal detection using excitation-scanning hyperspectral imaging. Excitation spectra were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Tunable filters had bandwidths between 13 and 17 nm. Scans utilized excitation wavelengths between 340 nm and 550 nm. Hyperspectral image stacks were generated and analyzed using ENVI and custom MATLAB scripts. Among channel consideration criteria were: number of channels, spectral range of scan, spacing of center wavelengths, and acquisition time. 

Coronary artery disease (CAD), or atherosclerosis, is responsible for nearly a third of all American deaths annually. Detection of plaques and differentiation of plaque stage remains a complicating factor for treatment. Classification of plaque before significant blockage or rupture could inform clinical decisions and prevent mortality. Current detection methods are either nonspecific, slow, or require the use of potentially harmful contrast agents. Recent advances in hyperspectral imaging could be used to detect changes in the autofluorescence of arteries associated with vessel remodeling and subsequent plaque formation and could detect and classify existing lesions. Here, we present data comparing spectral image characteristics of a mouse model designed to undergo vessel remodeling. C57Bl/6 mice underwent ligation of three of four caudal branches of the left common carotid artery (left external carotid, internal carotid, and occipital artery) with the superior thyroid artery left intact under IACUC approved protocol. Vessels were harvested at a variety of timepoints to compare degrees of remodeling, including 4 weeks and 5 months post-surgery. Immediately following harvest, vessels were prepared by longitudinal opening to expose the luminal surface to a 20X objective. A custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter arrary (Versachrome, Semrock, Inc.) were used to achieve spectral imaging. Excitation scans utilized wavelengths between 340 nm and 550 nm in 5 nm increments. Hyperspectral data were generated and analyzed with custom Matlab scripts and visualized in ENVI. Preliminary data suggest consistent spectral features associated with control and remodeled vessels. © (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. 

Ca2+ and cAMP are ubiquitous second messengers known to differentially regulate a variety of cellular functions over a wide range of timescales. Studies from a variety of groups support the hypothesis that these signals can be localized to discrete locations within cells, and that this subcellular localization is a critical component of signaling specificity. However, to date, it has been difficult to track second messenger signals at multiple locations within a single cell. This difficulty is largely due to the inability to measure multiplexed florescence signals in real time. To overcome this limitation, we have utilized both emission scan- and excitation scan-based hyperspectral imaging approaches to track second messenger signals as well as labeled cellular structures and/or proteins in the same cell. We have previously reported that hyperspectral imaging techniques improve the signal-to-noise ratios of both fluorescence and FRET measurements, and are thus well suited for the measurement of localized second messenger signals. Using these approaches, we have measured near plasma membrane and near nuclear membrane cAMP signals, as well as distributed signals within the cytosol, in several cell types including airway smooth muscle, pulmonary endothelial, and HEK-293 cells. We have also measured cAMP and Ca2+ signals near autofluorescent structures that appear to be golgi. Our data demonstrate that hyperspectral imaging approaches provide unique insight into the spatial and kinetic distributions of cAMP and Ca2+ signals in single cells.