Recently, phasor approach has emerged as a powerful tool for extracting fluorescence lifetime and has been utilized as a biochemical component analyzing tool without complicated fitting algorithms. In this study, we propose the new method to obtain phasors from directly sampled waveforms. With deconvolution using optically obtained instrumental response function (IRF), fluorescence lifetime can be successfully measured with high precision (~ 40 psec). Cells under the various metabolic conditions were imaged through label-free fluorescence lifetime imaging microscopy with targeting nicotinamide adenine dinucleotide (NADH) and their phasors exhibited distinct clusters on phasor plots corresponding to different culturing conditions.
KEYWORDS: Optical coherence tomography, Resolution enhancement technologies, Denoising, Signal processing, Systems modeling, In vivo imaging, Image processing, Gallium nitride, Fourier transforms, Signal to noise ratio
We proposed a dual-GAN-based deep learning to enhance resolution and reduce noise of optical coherence tomography (OCT). The dual GAN was designed with a model that enhances axial resolution and a model that enhances lateral resolution and reduces noise. We demonstrated improvements on the swine coronary artery data used for training, and further validated the performance on other sample data acquired in other systems. Through this, not only the performance but also the feasibility of independent application to a specific system or sample was verified. The current approach will be highly helpful in overcoming existing limitations of OCT.
Photoactivation is a promising theranostic tool to image and stabilize the atherosclerotic plaque by apoptosis induction in macrophages or other vascular cells; however, lack of effective drugs and mechanistic understanding hinder its clinical application for cardiovascular disease. Here, we developed the macrophage targeted photosensitizer delivery strategy and demonstrated that imaging assisted light activation reduced inflammation and burden of atherosclerotic plaques. Mechanistically, targeted photoactivation induced autophagy and increased MerTK expression in carotid atheroma as early as 1 day, and had 2-fold increase in macrophage-associated apoptotic cells, indicating efferocytosis enhancement. This multifunctional photoactivatable theranostic strategy could confer a promising tool for high-risk plaques.
We developed a high-precision multispectral fluorescence lifetime imaging microscopy (FLIM) for label-free immune-histologic imaging of atherosclerotic plaques. With images of fluorescence lifetimes and intensity ratios between different channels, we could characterize various plaque components of coronary arteries that are related to immunohistochemistry results. Correlative FLIM-immunohistochemistry validation revealed significant associations between plaque components and multispectral FLIM parameters. The machine learning algorithm, trained with co-registered FLIM-immunohistochemistry datasets, allowed automated visualization of multiple atherosclerotic components from FLIM image of an unstained section. We anticipate that the multispectral FLIM can be widely used to assess biochemical components of various biological tissues, including atherosclerotic plaques.
Intravascular optical coherence tomography-fluorescence lifetime imaging (OCT-FLIm) provides co-registered structural and biochemical information of atherosclerotic plaques in a label-free manner. For intuitive image interpretation of OCT-FLIm, herein, we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features. Using dataset from in vivo atheromatous swine models, the classification accuracy was >92% for each plaque component according the five-fold cross validation. This highly translatable imaging strategy will open a new avenue for clinical intracoronary assessment of high-risk plaques.
Multimodal optical coherence tomography (OCT) techniques are promising diagnostic tools to accurately assess highrisk atherosclerotic plaques. For rapid translation into clinical practice, the techniques should be performed through an intravascular imaging catheter without exogenous contrast agents under the same procedures as conventional imaging. In this study, we developed a label-free, multispectral, and catheter-based imaging system to simultaneously visualize the morphological and compositional information of coronary plaques by combining fluorescence lifetime imaging (FLIm) and OCT. Using a broadband hybrid optical rotary joint and a dual-modal imaging catheter, intravascular combined FLIm-OCT imaging was safely performed in an in vivo atherosclerotic coronary artery of atherosclerotic swine models without any imaging agent. Along with detailed coronary microstructure by OCT, the multispectral FLIm could accurately visualize fluorescence lifetime signature of key biochemical components of plaque in vivo (lipid, macrophage, and fibrous tissue) when comparing the corresponding histopathological stained-sections and ex vivo FLIm microscopy images. Especially, significant differences in fluorescence lifetime distribution were noted between lipid and macrophage (p < 0.0001), which were mostly indistinguishable with standalone OCT. Also, fluorescence lifetime distributions were significantly different according to plaque types (normal, fibrous vs. lipid-rich inflamed plaque, (p < 0.0001). With these statistical differences in plaque types and components, lipid distribution characterization and inflammation level estimation were provided in a pixel-by-pixel manner for the further assessment of the high-risk atherosclerotic plaque. This highly translatable imaging strategy can offer new opportunity for clinical intracoronary detection of high-risk plaques and will be a promising next-generation multimodal OCT technique.
Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the internal structures of walls of coronary arteries in vivo. However, accurate characterization of atherosclerotic plaques with gray-scale IV-OCT images is often limited by various intrinsic artifacts. In this study, we present an algorithm for characterizing lipid-rich plaques with a spectroscopic OCT technique based on a Gaussian center of mass (GCOM) metric. The GCOM metric, which reflects the absorbance properties of lipids, was validated using a lipid phantom. In addition, the proposed characterization method was successfully demonstrated in vivo using an atherosclerotic rabbit model and was found to have a sensitivity and specificity of 94.3% and 76.7% for lipid classification, respectively.
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