As we know, fluorescence lifetime imaging has demonstrated the ability to accurately detect materials and tissue constituents1–3. Current fluorescence lifetime systems rely on accurate temporal sampling to capture the tails of the decaying emission. These data are often fit to an exponential decay model3,4. Although these methodologies are powerful tools but they are often implemented as point measurement systems and require significant postprocessing to compute decay times or coefficients5–8. In some applications these factors can hinder clinical translation. Based on these observations, our group has developed algorithms and built simple, fast, and wide field imaging system9,10. This method uses a gated charge-coupled device (CCD) and a liquid light cable guided LED to compare the decay-time image intensity vs excited state image intensity, thus generating a spatially resolved maps of relative differences in autofluorescence decay of tissue constituents. This approach ensures very fast updating speed (< 2 sec per frame), big field of view (20 mm x 20 mm), excellent depth of field (up to 6 mm) for surface curvature of interested target at reasonable working distance (~50 mm). This innovative imaging system has a temporal resolution of 0.16 nanosecond, spatial resolution of 70 μm and has proved the capability to differentiate visibly similar tissue types, which has been validated with both fluorescent dyes and ex vivo human tissue samples in comparison to commercially available FLIM microscope. This work establishes a foundation to confirm the utility of our upgraded DOCI system for intraoperative tissue differentiating/imaging. Validation with a larger number of samples is currently ongoing.
Dynamic optical contrast imaging (DOCI) is a novel optical imaging technology that rapidly generates image contrast from measurements of aggregate endogenous fluorescence lifetime in a clinically meaningful field of view. Recently, our use of this system in both human ex-vivo and in-vivo specimens generated statistically significant contrast between tumor and adjacent normal tissue in biopsies taken from patients undergoing surgery for head and neck squamous cell carcinoma and primary hyperthyroidism. In this work we evaluated the components and resolution of our next-generation DOCI system. We also standardized the quantitative output of our system against the fluorescence lifetime values of three dye standards with monoexponential decay using a commercial Leica two-photon fluorescence lifetime imaging microscope. Significantly, our system continued to demonstrate clinically meaningful contrast between tissue samples with multiexponential decay in near real-time.
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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