23 March 2016 Single-pixel hyperspectral imaging for real-time cancer detection: detecting damage in ex vivo porcine tissue samples
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Abstract
We are developing a single-pixel hyperspectral imaging system based on compressive sensing that acquires spatial and spectral information simultaneously. Our spectral imaging system uses autofluorescencent emission from collagen (400 nm) and NAD(P)H (475 nm), as well as, differences in the optical reflectance spectra as diagnostics for differentiating between healthy and diseased tissue. In this study, we demonstrate the ability of our imaging system to discriminate between healthy and damaged porcine epidermal tissue. Healthy porcine epidermal tissue samples (n=11) were imaged ex vivo using our hyperspectral system. The amount of NAD(P)H emission and the reflectance properties were approximately constant across the surface of healthy tissue samples. The tissue samples were then thermally damaged using an 1850 nm thulium fiber laser and re-imaged after laser irradiation. The damaged regions were clearly visible in the hyperspectral images as the thermal damage altered the fluorescent emission of NAD(P)H and changed the scattering properties of the tissue. The extent of the damaged regions was determined based on the hyperspectral images and these estimates were compared to damage extents measured in white light images acquired with a traditional camera. The extent of damage determined via hyperspectral imaging was in good agreement with estimates based on white light imaging indicating that our system is capable of differentiating between healthy and damaged tissue. Possible applications of our single pixel hyperspectral imaging system range from real-time determination of tumor margins during surgery to the use of this technique in the pathology lab to aid with cancer diagnosis and staging.
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Joseph Peller, Joseph Peller, Faramarz Farahi, Faramarz Farahi, Susan R. Trammell, Susan R. Trammell, } "Single-pixel hyperspectral imaging for real-time cancer detection: detecting damage in ex vivo porcine tissue samples", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910O (23 March 2016); doi: 10.1117/12.2211408; https://doi.org/10.1117/12.2211408
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