Direct frequency domain fluorescence lifetime imaging using simultaneous ultraviolet and visible excitation.

Due to the complexity, limited practicality, and cost of conventional fluorescence lifetime imaging/microscopy (FLIM) instrumentation, FLIM adoption has been mostly limited to academic settings. We present a novel point scanning frequency-domain (FD) FLIM instrumentation design capable of simultaneous multi-wavelength excitation, simultaneous multispectral detection, and sub-nanosecond to nanosecond fluorescence lifetime estimation. Fluorescence excitation is implemented using intensity-modulated CW diode lasers that are available in a selection of wavelengths spanning the UV-VI-NIR range (375-1064 nm). Digital laser intensity modulation was adopted to enable simultaneous frequency interrogation at the fundamental frequency and corresponding harmonics. Time-resolved fluorescence detection is implemented using low-cost, fixed-gain, narrow bandwidth (100 MHz) avalanche photodiodes, thus, enabling cost-effective fluorescence lifetime measurements at multiple emission spectral bands simultaneously. Synchronized laser modulation and fluorescence signal digitization (250 MHz) is implemented using a common field-programmable gate array (FPGA). This synchronization reduces temporal jitter, which simplifies instrumentation, system calibration, and data processing. The FPGA also allows for the implementation of the real-time processing of the fluorescence emission phase and modulation at up to 13 modulation frequencies (processing rate matching the sampling rate of 250 MHz). Rigorous validation experiments have demonstrated the capabilities of this novel FD-FLIM implementation to accurately measure fluorescence lifetimes in the range of 0.5-12 ns. In vivo endogenous, dual-excitation (375nm/445nm), multispectral (four bands) FD-FLIM imaging of human skin and oral mucosa at 12.5 kHz pixel rate and room-light conditions was also successfully demonstrated. This versatile, simple, compact, and cost-effective FD-FLIM implementation will facilitate the clinical translation of FLIM imaging and microscopy.

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.

OBJECTIVE: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. METHODS: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. RESULTS: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. CONCLUSION: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

Significance: Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical coherence tomography (IV-OCT) imaging has been used for plaque assessment. The interpretation of the IV-OCT images is performed visually, which is burdensome and requires highly trained physicians for accurate plaque identification. Aim: Our study aims to provide high throughput lipid-laden plaque identification that can assist in vivo imaging by offering faster screening and guided decision making during percutaneous coronary interventions. Approach: An A-line-wise classification methodology based on time-series deep learning is presented to fulfill this aim. The classifier was trained and validated with a database consisting of IV-OCT images of 98 artery sections. A trained physician with expertise in the analysis of IV-OCT imaging provided the visual evaluation of the database that was used as ground truth for training and validation. Results: This method showed an accuracy, sensitivity, and specificity of 89.6%, 83.6%, and 91.1%, respectively. This deep learning methodology has the potential to increase the speed of lipid-laden plaques identification to provide a high throughput of more than 100 B-scans/s. Conclusions: These encouraging results suggest that this method will allow for high throughput video-rate atherosclerotic plaque assessment through automated tissue characterization for in vivo imaging by providing faster screening to assist in guided decision making during percutaneous coronary interventions.

Clinical label-free endoscopic imaging of biochemical and metabolic autofluorescence biomarkers of benign, precancerous, and cancerous oral lesions.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis. This study aimed to identify novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer that could be clinically imaged using novel multispectral autofluorescence lifetime imaging (maFLIM) endoscopy technologies. In vivo maFLIM clinical endoscopic images of benign, precancerous, and cancerous lesions from 67 patients were acquired using a novel maFLIM endoscope. Widefield maFLIM feature maps were generated, and statistical analyses were applied to identify maFLIM features providing contrast between dysplastic/cancerous vs. benign oral lesions. A total of 14 spectral and time-resolved maFLIM features were found to provide contrast between dysplastic/cancerous vs. benign oral lesions, representing novel biochemical and metabolic autofluorescence biomarkers of oral epithelial dysplasia and cancer. To the best of our knowledge, this is the first demonstration of clinical widefield maFLIM endoscopic imaging of novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer, supporting the potential of maFLIM endoscopy for early detection of oral cancer.

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning.

SIGNIFICANCE: Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. AIM: To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. APPROACH: We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. RESULTS: Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. CONCLUSIONS: Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Enhanced detection of oral dysplasia by structured illumination fluorescence lifetime imaging microscopy.

We demonstrate that structured illumination microscopy has the potential to enhance fluorescence lifetime imaging microscopy (FLIM) as an early detection method for oral squamous cell carcinoma. FLIM can be used to monitor or detect changes in the fluorescence lifetime of metabolic cofactors (e.g. NADH and FAD) associated with the onset of carcinogenesis. However, out of focus fluorescence often interferes with this lifetime measurement. Structured illumination fluorescence lifetime imaging (SI-FLIM) addresses this by providing depth-resolved lifetime measurements, and applied to oral mucosa, can localize the collected signal to the epithelium. In this study, the hamster model of oral carcinogenesis was used to evaluate SI-FLIM in premalignant and malignant oral mucosa. Cheek pouches were imaged in vivo and correlated to histopathological diagnoses. The potential of NADH fluorescence signal and lifetime, as measured by widefield FLIM and SI-FLIM, to differentiate dysplasia (pre-malignancy) from normal tissue was evaluated. ROC analysis was carried out with the task of discriminating between normal tissue and mild dysplasia, when changes in fluorescence characteristics are localized to the epithelium only. The results demonstrate that SI-FLIM (AUC = 0.83) is a significantly better (p-value = 0.031) marker for mild dysplasia when compared to widefield FLIM (AUC = 0.63).

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach.

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors.

Development and Characterization of PLGA-Based Multistage Delivery System for Enhanced Payload Delivery to Targeted Vascular Endothelium.

Vascular-targeted drug delivery remains an attractive platform for therapeutic and diagnostic interventions in human diseases. This work focuses on the development of a poly-lactic-co-glycolic-acid (PLGA)-based multistage delivery system (MDS). MDS consists of two stages: a micron-sized PLGA outer shell and encapsulated drug-loaded PLGA nanoparticles. Nanoparticles with average diameters of 76, 119, and 193 nm are successfully encapsulated into 3-6 µm MDS. Sustained in vitro release of nanoparticles from MDS is observed for up to 7 days. Both MDS and nanoparticles arebiocompatible with human endothelial cells. Sialyl-Lewis-A (sLeA ) is successfully immobilized on the MDS and nanoparticle surfaces to enable specific targeting of inflamed endothelium. Functionalized MDS demonstrates a 2.7-fold improvement in endothelial binding compared to PLGA nanoparticles from human blood laminar flow. Overall, the presented results demonstrate successful development and characterization of MDS and suggest that MDS can serve as an effective drug carrier, which can enhance the margination of nanoparticles to the targeted vascular wall.

Methylene blue-filled biodegradable polymer particles as a contrast agent for optical coherence tomography.

Optical coherence tomography (OCT) images largely lack molecular information or molecular contrast. We address that issue here, reporting on the development of biodegradable micro and nano-spheres loaded with methylene blue (MB) as molecular contrast agents for OCT. MB is a constituent of FDA approved therapies and widely used as a dye in off-label clinical applications. The sequestration of MB within the polymer reduced toxicity and improved signal strength by drastically reducing the production of singlet oxygen and leuco-MB. The former leads to tissue damage and the latter to reduced image contrast. The spheres are also strongly scattering which improves molecular contrast signal localization and enhances signal strength. We demonstrate that these contrast agents may be imaged using both pump-probe OCT and photothermal OCT, using a 830 nm frequency domain OCT system and a 1.3 µm swept source OCT system. We also show that these contrast agents may be functionalized and targeted to specific receptors, e.g. the VCAM receptor known to be overexpressed in inflammation.

Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions.

INTRODUCTION: Incomplete head and neck cancer resection occurs in up to 85% of cases, leading to increased odds of local recurrence and regional metastases; thus, image-guided surgical tools for accurate, in situ and fast detection of positive margins during head and neck cancer resection surgery are urgently needed. Oral epithelial dysplasia and cancer development is accompanied by morphological, biochemical, and metabolic tissue and cellular alterations that can modulate the autofluorescence properties of the oral epithelial tissue. OBJECTIVE: This study aimed to test the hypothesis that autofluorescence biomarkers of oral precancer and cancer can be clinically imaged and quantified by means of multispectral fluorescence lifetime imaging (FLIM) endoscopy. METHODS: Multispectral autofluorescence lifetime images of precancerous and cancerous lesions from 39 patients were imaged in vivo using a novel multispectral FLIM endoscope and processed to generate widefield maps of biochemical and metabolic autofluorescence biomarkers of oral precancer and cancer. RESULTS: Statistical analyses applied to the quantified multispectral FLIM endoscopy based autofluorescence biomarkers indicated their potential to provide contrast between precancerous/cancerous vs. healthy oral epithelial tissue. CONCLUSION: To the best of our knowledge, this study represents the first demonstration of label-free biochemical and metabolic clinical imaging of precancerous and cancerous oral lesions by means of widefield multispectral autofluorescence lifetime endoscopy. Future studies will focus on demonstrating the capabilities of endogenous multispectral FLIM endoscopy as an image-guided surgical tool for positive margin detection during head and neck cancer resection surgery.

Direct frequency domain fluorescence lifetime imaging using field programmable gate arrays for real time processing.

Frequency domain (FD) fluorescence lifetime imaging (FLIM) involves the excitation of the sample of interest with a modulated light source and digitization of the fluorescence emission for further analysis. Traditional FD-FLIM systems use heterodyne or homodyne detection, where the excitation light source and detector are modulated at specific frequency(s). More recently, FD-FLIM systems that use reflection of the light source as a trigger or phase reference for lifetime calculations have been developed. These detection schemes, however, require extra components that increase the cost and complexity of the FD-FLIM system. Here, we report a novel FD-FLIM detection scheme whereby the light source modulation and emission digitization are implemented using Field Programmable Gate Arrays (FPGAs), and fixed gain avalanche photodiodes are used for fluorescence detection. The reported FD-FLIM system was designed for probing nanosecond lifetime fluorophores (2-10 ns) at three emission bands simultaneously. The system utilizes a 375 nm diode laser for excitation at multiple simultaneous modulation frequencies (between 1 MHz and 83 MHz, bandwidth limited intentionally by using a lowpass filter) and three fixed gain avalanche photodiodes for simultaneous detection of three emission bands: 405/20 nm, 440/40 nm, and 525/50 nm (center/FWHM). Real-time computation of the modulation and phase lifetimes is simply performed by direct application of the discrete Fourier transform (max. of 10 frequencies) to the digitized fluorescence emission signals. The accuracy and sensitivity of this novel FD-FLIM detection scheme was demonstrated by imaging standard fluorophores and ex vivo unfixed human coronary artery tissue samples.

Multispectral autofluorescence dermoscope for skin lesion assessment.

Basal cell carcinoma (BCC) is the most common type of skin cancer. Diagnosis and edge assessment of BCC lesions are based on clinical and dermoscopy evaluation, which are strongly dependent on the expertise and training of the physician. There is a high rate of underdiagnosis because BCC is frequently confused with certain common benign lesions and is often indistinguishable from the surrounding healthy tissue. In the present study, a multispectral fluorescence lifetime imaging (FLIm) dermoscopy system, designed for imaging and analyzing the autofluorescence emission of skin tissue, was used to image thirty-eight patients with diagnosed nodular BCC (nBCC) lesions, using clinically acceptable levels of excitation light exposure. With this system, skin autofluorescence was imaged simultaneously using three emission bands: 390 ±â€¯20 nm, 452 ±â€¯22 nm, and >496 nm, preferentially targeting collagen, NADH, and FAD autofluorescence, respectively. Statistical classifiers based on FLIm features developed to discriminate BCC from healthy tissue showed promising performance (ROC area-under-the-curve of 0.82). This study demonstrates the feasibility of clinically performing multispectral endogenous FLIm dermoscopy providing baseline results indicating the potential of this technology as an image-guided tool to improve the delineation of nBCC during surgical lesion resection.

Intravascular optical coherence tomography method for automated detection of macrophage infiltration within atherosclerotic coronary plaques.

BACKGROUND AND AIMS: Significant macrophages infiltration in advanced atherosclerotic plaques promotes acute coronary events. Hence, the clinical imaging of macrophage content in coronary atherosclerotic plaques could potentially aid in identifying patients most at risk of future acute coronary events. The aim of this study was to introduce and validate a simple intravascular optical coherence tomography (IV-OCT) image processing method for automated, accurate and fast detection of macrophage infiltration within coronary atherosclerotic plaques. METHODS: This method calculates the ratio of the normalized-intensity standard deviation (NSD) values estimated over two axially-adjacent regions of interest in an IV-OCT cross-sectional image (B-scan). When applied to entire IV-OCT B-scans, this method highlights plaque areas with high NSD ratio values (NSDRatio), which was demonstrated to be correlated with the degree of coronary plaque macrophage infiltration. RESULTS: Using an optimized NSDRatio threshold value, coronary plaque macrophage infiltration could be detected with ~88% sensitivity and specificity in a database of 28 IV-OCT scans from postmortem coronary segments. For comparison, using an optimized NSD threshold value, considered the standard IV-OCT signature for macrophages, coronary plaque macrophage infiltration could be detected with only ~55% sensitivity and specificity. CONCLUSIONS: The proposed NSDRatio method significantly increases the sensitivity and specificity for the detection of coronary plaque macrophage infiltration compared to the standard NSD method. This computationally efficient method can be seamlessly implemented within standard IV-OCT imaging systems for in-vivo real-time imaging of macrophage content in coronary plaques, which could potentially aid in identifying patients most at risk of future acute coronary events.

AI-Assisted In Situ Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography.

PURPOSE: In glioma surgery, it is critical to maximize tumor resection without compromising adjacent noncancerous brain tissue. Optical coherence tomography (OCT) is a noninvasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here, we report a novel artificial intelligence (AI)-assisted method for automated, real-time, in situ detection of glioma infiltration at high spatial resolution.Experimental Design: Volumetric OCT datasets were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either noncancerous or glioma-infiltrated on the basis of histopathology evaluation of the tissue specimens (gold standard). Labeled OCT images from 12 patients were used as the training dataset to develop the AI-assisted OCT-based method for automated detection of glioma-infiltrated brain tissue. Unlabeled OCT images from the other 9 patients were used as the validation dataset to quantify the method detection performance. RESULTS: Our method achieved excellent levels of sensitivity (∼100%) and specificity (∼85%) for detecting glioma-infiltrated tissue with high spatial resolution (16 µm laterally) and processing speed (∼100,020 OCT A-lines/second). CONCLUSIONS: Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on estimating the tissue optical attenuation coefficient from the OCT signal, which requires sacrificing spatial resolution to increase signal quality, and performing systematic calibration procedures using tissue phantoms. By overcoming these major challenges, our AI-assisted method will enable implementing practical OCT-guided surgical tools for continuous, real-time, and accurate intraoperative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for patients with glioma.

Automated detection of superficial macrophages in atherosclerotic plaques using autofluorescence lifetime imaging.

BACKGROUND AND AIMS: Macrophages play an important role in the development and destabilization of advanced atherosclerotic plaques. Hence, the clinical imaging of macrophage content in advanced plaques could potentially aid in identifying patients most at risk of future clinical events. The lifetime of the autofluorescence emission from atherosclerotic plaques has been correlated with lipids and macrophage accumulation in ex vivo human coronary arteries, suggesting the potential of intravascular endogenous fluorescence or autofluorescence lifetime imaging (FLIM) for macrophage imaging. The aim of this study was to quantify the accuracy of the coronary intima autofluorescence lifetime to detect superficial macrophage accumulation in atherosclerotic plaques. METHODS: Endogenous FLIM imaging was performed on 80 fresh postmortem coronary segments from 23 subjects. The plaque autofluorescence lifetime at an emission spectral band of 494 ±â€¯20.5 nm was used as a discriminatory feature to detect superficial macrophage accumulation in atherosclerotic plaques. Detection of superficial macrophage accumulation in the imaged coronary segments based on immunohistochemistry (CD68 staining) evaluation was taken as the gold standard. Receiver Operating Characteristic (ROC) curve analysis was applied to select an autofluorescence lifetime threshold value to detect superficial macrophages accumulation. RESULTS: A threshold of 6 ns in the plaque autofluorescence lifetime at the emission spectral band of 494 ±â€¯20.5 nm was applied to detect plaque superficial macrophages accumulation, resulting in ∼91.5% accuracy. CONCLUSIONS: This study demonstrates the capability of endogenous FLIM imaging to accurately identify superficial macrophages accumulation in human atherosclerotic plaques, a key biomarker of atherosclerotic plaque vulnerability.

Endogenous Fluorescence Lifetime Imaging (FLIM) Endoscopy For Early Detection Of Oral Cancer And Dysplasia.

We have performed a pilot clinical study, in which multispectral endogenous fluorescence (or autofluorescence) lifetime imaging (FLIM) was performed on clinically suspicious oral lesions of 73 patients undergoing tissue biopsy for oral dysplasia and cancer diagnosis. The results from this pilot study indicated that mild-dysplasia and early stage oral cancer could be detected from benign lesions using a computed aided diagnosis system developed based on biochemical and metabolic biomarkers derived from the endogenous FLIM images. The diagnostic performance of this novel FLIM clinical tool was estimated using a leave-onepatient-out cross-validation approach, which reported levels of sensitivity >90%, specificity >85%, and Area Under the Receiving Operating Curve (ROC-AUC) >0.9.

Optically sectioned wide-field fluorescence lifetime imaging microscopy enabled by structured illumination.

In this paper, we demonstrate the ability of structured illumination microscopy to enhance the ability of fluorescence lifetime imaging to resolve fluorescence lifetimes in relatively thick samples that possess distinct but spectrally overlapping fluorescent layers. Structured illumination fluorescent lifetime imaging microscopy (SI-FLIM) is shown to be able to accurately reconstruct lifetime values in homogenous fluorophore samples (POPOP, NADH, and FAD) as well as accurately measure fluorescent lifetime in two layer models that are layered with NADH/FAD over POPOP, where NADH/FAD and POPOP have spectral overlap. Finally, the ability of SI-FLIM was demonstrated in a hamster cheek pouch ex vivo to show that more accurate lifetimes could be measured for each layer of interest in the oral mucosa (epithelium and submucosa).