22 February 2017 Optimization and applications of an excitation-scanning hyperspectral imaging system
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Abstract
Currently, the majority of microscopic and endoscopic technologies utilize white light illumination. For a number of applications, hyper-spectral imaging can be shown to have significant improvements over standard white-light imaging techniques. This is true for both microscopy and in vivo imaging. However, hyperspectral imaging methods have suffered from slow application times. Often, minutes are required to gather a full imaging stack. Here we will describe the system and evaluate optimizations and applications of a novel excitation-scanning hyperspectral imaging system. We have developed and are optimizing a novel approach called excitation-scanning hyperspectral imaging that provides an order of magnitude increased signal strength. Optimization of the light path, optical components and illumination sources have allowed us to achieve high speed image acquisition. This high speed allows for potential live video acquisition. This excitation-scanning hyperspectral imaging technology has potential to impact a range of applications. The current system allows triggering of up to 16 wavelengths at less than 1 millisecond per image using digital strobing. Analog intensity control is also provided for a fully customizable excitation profile. A significant advantage of excitation scanning hyperspectral imaging is can identify multiple targets simultaneously in real time. We are optimizing the system to compare sensitivity and specificity of excitation-scanning hyperspectral imaging with pathology techniques. Finally, we are exploring utilizing this technology to measure cAMP distribution in three dimensions within a cell.
Conference Presentation
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Sam A. Mayes, Sam A. Mayes, Phiwat Klomkaew, Phiwat Klomkaew, Silas J. Leavesley, Silas J. Leavesley, Thomas C. Rich, Thomas C. Rich, } "Optimization and applications of an excitation-scanning hyperspectral imaging system", Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 1007609 (22 February 2017); doi: 10.1117/12.2252409; https://doi.org/10.1117/12.2252409
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