KEYWORDS: Luminescence, Tissues, Imaging systems, Data modeling, Convolution, Optical imaging, Mathematical modeling, Signal to noise ratio, Systems modeling, Statistical modeling
Dynamic Optical Contrast Imaging (DOCi) is an imaging technique that generates image contrast through ratiometric measurements of the autouflorescence decay rates of aggregate uorophores in tissue. This method enables better tissue characterization by utilizing wide-field signal integration, eliminating constraints of uniform illumination, and reducing time-intensive computations that are bottlenecks in the clinical translation of traditional fluorescence lifetime imaging. Previous works have demonstrated remarkable tissue contrast between tissue types in clinical human pilot studies [Otolaryngology-Head and Neck Surgery 157, 480 (2017)]. However, there are still challenges in the development of several subsystems, which results in existing works to use relative models. A comprehensive mathematical framework is presented to describe the contrast mechanism of the DOCi system to allow intraoperative quantitative imaging, which merits consideration for evaluation in measuring tissue characteristics in several important clinical settings.
Objective: DOCI is a novel imaging modality with the ability to detect variations in endogenous fluorophore lifetimes by illuminating tissue with pulsed ultraviolet (UV) light. We have previously shown that DOCI is capable of delineating tumor margins. Tissue macro-/micro-environments, however, vary with organ site and histology. We therefore sought to better characterize DOCI signal analysis within the varying subsites of the oral cavity in this ex-vivo animal model.
Design: Fresh ex-vivo oral cavity specimens (n=66) from three New Zealand white rabbits were harvested for pulsed UV illumination utilizing a 6-diode in-series DOCI system. Photons produced were detected and fluorophore lifetimes calculated over a specified, homogenous, region of interest. Specimen site, size, histology, and relative average DOCI values analyzed.
Results: 66 specimens produced over 2 million data points for fluorophore lifetime analysis. The oral tongue muscle, dentition, and mucosa from the dorsal tongue, floor of mouth, and hard palate all produced unique DOCI relative average values. Each subsite was found to be uniquely different from one another and produced statistically significant differences in DOCI value (p<0.05).
Conclusions: DOCI has the ability to distinguish subtle differences in oral cavity subsites following fresh ex vivo harvest. The fluorophore lifetime relative average values of each tissue is uniquely different posing a novel strategy for intra operative oncologic imaging, surveillance, and possibly aid in the workup of pre-cancerous lesions. Growing a repository of normal tissue subsites is crucial for integrating an automated real-time deep learning algorithm for rapid tissue analysis.
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