Multiphoton Grin Endoscopy For Real-Time Diagnosis Of Diseased Tissue
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Multiphoton microscopy has the potential to become a valuable tool for clinical diagnosis of tissue health. It has the ability to provide images with similar cellular and architectural tissue information to the gold standard for tissue diagnosis, histopathological analysis of biopsies. However, unlike histopathology, it can provide these images in real time in unstained and unprocessed tissue in vivo. Due to the depth limitations of multiphoton microscopy, endoscopic access to the tissue is required for this technology to be clinically useful. This dissertation details our efforts to translate multiphoton microscopy into the clinical field through the development of a GRIN lens based multiphoton endoscopic prototype. As compared to other endoscopic approaches, GRIN lenses, while rigid, provide several advantages, including small diameters (down to 0.350 mm), no need to miniaturize excitation and collection optics, low manufacturing costs and potential compatibility with existing biopsy instrumentation. In this dissertation we initially show that multiphoton imaging through long (up to 285 mm) GRIN lens endoscope systems is possible. We then design fabricate a portable, rigid endoscope system suitable for imaging unstained tissues, potentially deep within the body, using a GRIN lens system of 1 mm diameter and 8 cm length. The portable device is capable of imaging a ~200 [mu]m diameter field of view at 4 frames/s. The lateral and axial resolution in water is 0.85 [mu]m and 7.4 [mu]m respectively. We demonstrate the capabilities of our device through in vivo imaging of unstained tissues in live, anesthetized rats. We further show compatibility of this device with three photon excitation. Finally, we test the diagnostic capabilities of our prototype on human prostate cancer samples ex vivo. The presented results show great promise for GRIN endoscopy to become a valuable tool clinically both for the diagnosis of tissue health and to aid during surgeries by identifying tumor margins and other tissue architecture.
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Schaffer, Chris