Fluorescence lifetime imaging microscopy (FLIM) offers a noninvasive approach for characterizing the biochemical composition of biological tissue. In recent years, there has been an increasing interest in the application of multispectral FLIM for medical diagnosis. Central to the clinical translation of FLIM technology is the development of robust, fast, and cost-effective FLIM instrumentation suitable for in vivo tissue imaging. Unfortunately, the predominant multispectral FLIM approaches suffer from limitations that impede the development of high-speed instruments for in vivo applications. We present a cost-effective scanning multispectral FLIM implementation capable of achieving pixel rates on the order of tens of kilohertz, which will facilitate the evaluation of FLIM for in vivo applications.

Most pathological conditions elicit changes in the tissue optical response that may be interrogated by one or more optical imaging modalities. Any single modality typically only furnishes an incomplete picture of the tissue optical response, hence an approach that integrates complementary optical imaging modalities is needed for a more comprehensive non-destructive and minimally-invasive tissue characterization. We have developed a dual-modality system, incorporating optical coherence tomography (OCT) and fluorescence lifetime imaging microscopy (FLIM), that is capable of simultaneously characterizing the 3-D tissue morphology and its biochemical composition. The Fourier domain OCT subsystem, at an 830 nm center wavelength, provided high-resolution morphological volumetric tissue images with an axial and lateral resolution of 7.3 and 13.4 µm, respectively. The multispectral FLIM subsystem, based on a direct pulse-recording approach (upon 355 nm laser excitation), provided two-dimensional superficial maps of the tissue autofluorescence intensity and lifetime at three customizable emission bands with 100 µm lateral resolution. Both subsystems share the same excitation/illumination optical path and are simultaneously raster scanned on the sample to generate coregistered OCT volumes and FLIM images. The developed OCT/FLIM system was capable of a maximum A-line rate of 59 KHz for OCT and a pixel rate of up to 30 KHz for FLIM. The dual-modality system was validated with standard fluorophore solutions and subsequently applied to the characterization of two biological tissue types: postmortem human coronary atherosclerotic plaques, and in vivo normal and cancerous hamster cheek pouch epithelial tissue.

OBJECTIVE To investigate the potential of endogenous multispectral fluorescence lifetime imaging microscopy (FLIM) for biochemical characterization of human coronary atherosclerotic plaques. METHODS Endogenous multispectral FLIM imaging was performed on the lumen of 58 segments of postmortem human coronary artery. The fluorescence was separated into three emission bands targeting the three main arterial endogenous fluorophores (390±20 nm for collagen, 452±22.5 nm for elastin, and 550±20 for lipids). The fluorescence normalized intensity and average lifetime from each emission band was used to classify each pixel of an image as either “High-Collagen”, “High-Lipids” or “Low-Collagen/Lipids” via multiclass Fisher’s linear discriminant analysis. RESULTS Classification of plaques as either “High-Collagen”, “High-Lipids” or “Low-Collagen/Lipids” based on the endogenous multispectral FLIM was achieved with a sensitivity/specificity of 96/98%, 89/99%, and 99/99%, respectively, where histopathology served as the gold standard. CONCLUSION The endogenous multispectral FLIM approach we have taken, which can readily be adapted for in vivo intravascular catheter based imaging, is capable of reliably identifying plaques with high content of either collagen or lipids.

Abstract. Most studies evaluating the potential of optical coherence tomography (OCT) for the diagnosis of oral cancer are based on visual assessment of OCT B-scans by trained experts. Human interpretation of the large pool of data acquired by modern high-speed OCT systems, however, can be cumbersome and extremely time consuming. Development of image analysis methods for automated and quantitative OCT image analysis could therefore facilitate the evaluation of such a large volume of data. We report automated algorithms for quantifying structural features that are associated with the malignant transformation of the oral epithelium based on image processing of OCT data. The features extracted from the OCT images were used to design a statistical classification model to perform the automated tissue diagnosis. The sensitivity and specificity of distinguishing malignant lesions from benign lesions were found to be 90.2% and 76.3%, respectively. The results of the study demonstrate the feasibility of using quantitative image analysis algorithms for extracting morphological features from OCT images to perform the automated diagnosis of oral malignancies in a hamster cheek pouch model.

Early detection of cancer is key to reducing morbidity and mortality. Morphological and chemical biomarkers presage the transition from normal to cancerous tissue. We have developed a noninvasive imaging system incorporating optical coherence tomography (OCT) and fluorescence lifetime imaging microscopy (FLIM) into a single optical system for the first time, in order to acquire both sets of biomarkers. OCT can provide morphological images of tissue with high resolution, while FLIM can provide biochemical tissue maps. Coregistered OCT volumes and FLIM images have been acquired simultaneously in an in vivo hamster cheek pouch model of oral cancer. The OCT images indicate morphological biomarkers for cancer including thickening of the epithelial layer and loss of the layered structure. The FLIM images indicate chemical biomarkers including increased nicotinamide adenine dinucleotide and reduced collagen emission. While both sets of biomarkers can differentiate normal and cancerous tissue, we believe their combination will enable the discrimination of benign lesions possessing some of the indicated biomarkers, e.g., scarring or inflammation.