We have recently developed a novel, cost-effective, versatile, and practical frequency-domain (FD) FLIM implementation capable of simultaneous multi-wavelength excitation, simultaneous multispectral detection, and sub-nanosecond to nanosecond fluorescence lifetime estimation. This novel technology has been adopted to implement an FD-FLIM endoscopy system for label-free metabolic imaging of epithelial tissues. The FD-FLIM endoscopy system is capable of simultaneous excitation at two wavelengths (375-nm for NADH, 445-nm for FAD). Preliminary results indicate that established metabolic autofluorescence biomarkers of epithelial pre-malignancy and malignancy can be clinically imaged with this novel FD-FLIM endoscopy technology. Future work will focus on exploring clinical applications of label-free metabolic imaging using this novel FD-FLIM endoscopy technology.
We report a new approach for label-free metabolic imaging of oral lesions using a novel fluorescence lifetime imaging endoscopy (FLIMEnd) system. The FLIMEnd system is capable of simultaneous dual-wavelength excitation and fluorescence detection at four emission spectral bands. Preliminary results indicate that established metabolic autofluorescence biomarkers of oral pre-malignancy and malignancy can be clinically imaged with this novel FLIMEnd technology. Future work will focus on identifying both established and novel autofluorescence biomarkers of oral pre-cancer and cancer that can be used to develop machine-learning driven FLIMEnd systems for early detection of oral cancer.
Increased cellular metabolic activity can be quantified by imaging the oral tissue autofluorescence originated from NADH and FAD. Clinical label-free metabolic imaging of oral epithelial cancer based on 375-nm excitation maFLIM endoscopy was recently demonstrated in patients presenting oral malignant lesions. A recently upgraded maFLIM endoscopy system enables simultaneous autofluorescence excitation at 375 nm (for NADH) and 445 nm (for FAD). This maFLIM endoscope is being used to clinically image healthy, benign, precancerous and cancerous oral lesions in oral health care settings. Comprehensive statistical analysis will be performed to identify new metabolic and biochemical autofluorescence biomarkers of oral epithelial cancer.
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