Python-controlled multimodal UV-VIS hyperspectral imaging system

Significance: Traumatic and chronic wound healing is a complex process that often requires careful observation by healthcare professionals. Noninvasive optical technologies such as hyperspectral reflectance imaging and tissue autofluorescence imaging detect changes in optical properties related to physiological parameters such as tissue perfusion. Macroscopic hyperspectral systems with a large field of view to visualize tissue structure and perfusion are well suited for wound care. Aim: Here, a custom-built benchtop hyperspectral and autofluorescence imaging system (HySAF) is optimized to measure tissue optical properties. It operates using a custom Python script to allow precise control over acquisition parameters. Approach: HySAF was built using off-the-shelf components, and Python code was written to control primary devices. HySAF's performance was evaluated by imaging oxygenated and deoxygenated blood samples, scattering induced by nanoparticles, scorpion autofluorescence, and rat wounds. Results: pixel resolution and 10 nm spectral resolution from 420 to 730 nm. Changes in absorbance were observed during blood deoxygenation, nanoparticle presence in cells, and healing wound tissue in rats, while autofluorescence distinguished anatomical parts of the scorpion. Conclusion: HySAF is capable of detecting changes in sample fluorescence and absorbance across visible light with large FOV and could be implemented in clinical applications such as monitoring and assessing wound healing.