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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. 

Changes in the multi-level physical structure of biological features going from cellular to tissue level composition is a key factor in many major diseases. However, we are only beginning to understand the role of these structural changes because there are few dedicated multiscale imaging platforms with sensitivity at both the cellular and macrostructural spatial scale. A single platform reduces bias and complications from multiple sample preparation methods and can ease image registration. In order to address these needs, we have developed a multiscale imaging system using a range of imaging modalities sensitive to tissue composition: Ultrasound, Second Harmonic Generation Microscopy, Multiphoton Microscopy, Optical Coherence Tomography, and Enhanced Backscattering. This paper details the system design, the calibration for each modality, and a demonstration experiment imaging a rabbit eye.