MURIN: Multimodal Retinal Imaging and Navigated-laser-delivery for dynamic and longitudinal tracking of photodamage in murine models.

Laser-induced photodamage is a robust method for investigating retinal pathologies in small animals. However, aiming of the photocoagulation laser is often limited by manual alignment and lacks real-time feedback on lesion location and severity. Here, we demonstrate a multimodality OCT and SLO ophthalmic imaging system with an image-guided scanning laser lesioning module optimized for the murine retina. The proposed system enables targeting of focal and extended area lesions under OCT guidance to benefit visualization of photodamage response and the precision and repeatability of laser lesion models of retinal injury.

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.

Real-time OCT image denoising using a self-fusion neural network.

Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.

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.

Simultaneous label-free autofluorescence and multi-harmonic imaging reveals in vivo structural and metabolic changes in murine skin.

Simultaneous quantification of multifarious cellular metabolites and the extracellular matrix in vivo has been long sought. Simultaneous label-free autofluorescence and multi-harmonic (SLAM) microscopy has achieved simultaneous four-channel nonlinear imaging to study tissue structure and metabolism. In this study, we implemented two laser systems and directly compared SLAM microscopy with conventional two-photon microscopy for in vivo imaging. We found that three-photon imaging of adenine dinucleotide (phosphate) (NAD(P)H) in SLAM microscopy using our tailored laser source provided better resolution, contrast, and background suppression than conventional two-photon imaging of NAD(P)H. We also integrated fluorescence lifetime imaging with SLAM microscopy, and enabled differentiation of free and bound NAD(P)H. We imaged murine skin in vivo and showed that changes in tissue structure, cell dynamics, and metabolism can be monitored simultaneously in real-time. We also discovered an increase in metabolism and protein-bound NAD(P)H in skin cells during the early stages of wound healing.

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.

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).

Automatic classification of atherosclerotic plaques imaged with intravascular OCT.

Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present a novel computational method for automated IV-OCT plaque characterization. This method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The performance of our proposed method was evaluated with IV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. Our results suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, this novel computational method has the potential to enable high throughput atherosclerotic plaque characterization.