Flexible wide‐field optical micro‐angiography based on Fourier‐domain multiplexed dual‐beam swept source optical coherence tomography

Wide‐field optical coherence tomography angiography (OCTA) is gaining interest in clinical imaging applications. In this pursuit, it is challenging to maintain the imaging resolution and sensitivity throughout the wide field of view (FoV). Here, we propose a novel method/system of dual‐beam arrangement and Fourier‐domain multiplexing to achieve wide‐field OCTA when imaging the uneven surface samples. The proposed system provides 2 separate FoVs, with flexibility that the imaging area, focus of the imaging beam and imaging depth range can be individually adjusted for each FoV, leading to either (1) increased system imaging FoV or (2) capability of targeting 2 regions of interests that locate at depths with large difference between each other. We demonstrate this novel method by employing 100 kHz laser source in a swept source OCTA to achieve an effective 200 kHz sweeping rate, covering a 22 × 22 mm FoV. The results are verified by a SS‐OCTA system employing a 200 kHz laser source, together with the experimental demonstrations when imaging whole brain vasculature in rodent models and skin blood perfusion in human fingers, show‐casing the capability of proposed system to image live large samples with complex surface topography.      

Jones matrix‐based speckle‐decorrelation angiography using polarization‐sensitive optical coherence tomography

We show that polarization‐sensitive optical coherence tomography angiography (PS‐OCTA) based on full Jones matrix assessment of speckle decorrelation offers improved contrast and depth of vessel imaging over conventional OCTA. We determine how best to combine the individual Jones matrix elements and compare the resulting image quality to that of a conventional OCT scanner by co‐locating and imaging the same skin locations with closely matched scanning setups. Vessel projection images from finger and forearm skin demonstrate the benefits of Jones matrix‐based PS‐OCTA. Our study provides a promising starting point and a useful reference for future pre‐clinical and clinical applications of Jones matrix‐based PS‐OCTA.     

Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation

Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.     

Inside Cover: A low‐cost,automated parasite diagnostic system via a portable,robotic microscope and deep learning (J. Biophotonics 9/2019)

A low‐cost, automated microscope is combined with machine learning to bring veterinary parasite diagnosis to the point of need. The authors present an inexpensive robotic microscope that automatically focuses, scans, and images a large area McMaster chamber. A deep learning image segmentation pipeline identifies and counts eggs of parasitic worms and single‐celled parasites in goats, dogs, and monkeys, yielding >96% diagnostic accuracy without the need for a trained user. Further details can be found in the article by Yaning Li, Rui Zheng, Yizhen Wu, et al. ( e201800410 ).

     

An automated framework to quantify areas of regional ischemia in retinal vascular diseases with OCT angiography

In this observational and cross‐sectional study, capillary nonperfusion (CNP) and vascular changes in branch retinal vein occlusion (BRVO, sample size [n] = 26) and choroidal neovascularization (CNV, n = 29) were evaluated. Subjects underwent imaging using Optical coherence tomography angiography (Angiovue OCTA, RTVue XR, Optovue Inc., Fremont, California). Local fractal analysis was applied to the OCTA images of superficial, deep and choriocapillaris layer. CNP area (BRVO eyes) and vascular parameters were computed using local fractal‐based method. Sensitivity and specificity of vascular parameters were assessed with receiver operating characteristics curve. Automated CNP area showed excellent agreement with manually quantified CNP areas in both superficial (intraclass coefficient [ICC] = 0.96) and deep (ICC = 0.96) layers. BRVO eyes showed significantly altered (P < .05) vascular parameters in both superficial and deep layer as compared to normal eyes (n = 30). CNVM eyes had significantly higher capillary free zones (P < .001) as compared to normal eyes. In normal vs BRVO eyes, vessel density and spacing between the large vessels had similar area under the curve (AUC) (P > .05) in both superficial (0.97 and 0.97, respectively) and deep layer (0.99 and 0.98, respectively). Further, capillary free zones showed high AUC (0.92) in differentiating CNV eyes from normal eyes.      

Polarization‐multiplexed,dual‐beam swept source optical coherence tomography angiography

A polarization‐multiplexed, dual‐beam setup is proposed to expand the field of view (FOV) for a swept source optical coherence tomography angiography (OCTA) system. This method used a Wollaston prism to split sample path light into 2 orthogonal‐polarized beams. This allowed 2 beams to shine on the cornea at an angle separation of ~14°, which led to a separation of ~4.2 mm on the retina. A 3‐mm glass plate was inserted into one of the beam paths to set a constant path length difference between the 2 polarized beams so the interferogram from the 2 beams are coded at different frequency bands. The resulting OCTA images from the 2 beams were coded with a depth separation of ~2 mm. A total of 5 × 5 mm² angiograms from the 2 beams were obtained simultaneously in 4 seconds. The 2 angiograms then were montaged to get a wider FOV of ~5 × 9.2 mm².      

Endoscopic optical coherence tomography angiography using a forward imaging piezo scanner probe

A forward imaging endoscope for optical coherence tomography angiography (OCTA) featuring a piezoelectric fiber scanner is presented. Imaging is performed with an optical coherence tomography (OCT) system incorporating an akinetic light source with a center wavelength of 1300 nm, bandwidth of 90 nm and A‐line rate of 173 kHz. The endoscope operates in contact mode to avoid motion artifacts, in particular, beneficial for OCTA measurements, and achieves a transversal resolution of 12 μm in air at a rigid probe size of 4 mm in diameter and 11.3 mm in length. A spiral scan pattern is generated at a scanning frequency of 360 Hz to sample a maximum field of view of 1.3 mm. OCT images of a human finger as well as visualization of microvasculature of the human palm are presented both in two and three dimensions. The combination of morphological tissue contrast with qualitative dynamic blood flow information within this endoscopic imaging approach potentially enables improved early diagnostic capabilities of internal organs for diseases such as bladder cancer.      

Automatic skin lesion area determination of basal cell carcinoma using optical coherence tomography angiography and a skeletonization approach: Preliminary results

Cutaneous blood flow plays a key role in numerous physiological and pathological processes and has significant potential to be used as a biomarker to diagnose skin diseases such as basal cell carcinoma (BCC). The determination of the lesion area and vascular parameters within it, such as vessel density, is essential for diagnosis, surgical treatment and follow‐up procedures. Here, an automatic skin lesion area determination algorithm based on optical coherence tomography angiography (OCTA) images is presented for the first time. The blood vessels are segmented within the OCTA images and then skeletonized. Subsequently, the skeleton is searched over the volume and numerous quantitative vascular parameters are calculated. The vascular density is then used to segment the lesion area. The algorithm is tested on both nodular and superficial BCC, and comparing with dermatological and histological results, the proposed method provides an accurate, non‐invasive, quantitative and automatic tool for BCC lesion area determination.     

Interplane bulk motion analysis and removal based on normalized cross‐correlation in optical coherence tomography angiography

Bulk motion seriously degrades the image quality of optical coherence tomography angiography (OCTA). Conventional correction methods focus on in‐plane displacement, while the bulk motion component perpendicular to B‐scans also introduces noise. This work first presents an evaluation of this component using a specific scan protocol and an approximate expression derived from peak‐normalized cross‐correlation values, and then quantitatively assesses how interplane bulk motion noise reduce the sensitivity of cross‐sectional angiograms. Finally, we developed a repetitive bulk motion correction method based on the estimated displacements and redundant volume scans. The correction does not require registration and angiogram reconstruction of low flow sensitivity frames, and the results of in vivo mice skin OCTA imaging experiments show that the proposed method can effectively reduce bulk motion noise caused by cardiac and respiratory motion and occasional shaking, and improve OCTA image quality, which has practical significance for clinical OCTA diagnosis and analysis.     

Machine learning in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.     

Optical coherence tomography angiography for mapping cerebral microvasculature based on normalized differentiation analysis

Optical coherence tomography angiography (OCTA) is a label‐free, noninvasive biomedical imaging modality for mapping microvascular networks and quantifying blood flow velocities in vivo. Simple computation and fast processing are critical for the OCTA in some applications. Herein, we report on a normalized differentiation method for mapping cerebral microvasculature with the advantages of simple analysis and high image quality, benefitting from computation of differentiation and characteristics of normalization. Normalized differentiation values are validated to have a nearly linear relationship with flow velocities in a range using a flow phantom. The measurements in a rat cerebral cortex show that the OCTA based on the normalized differentiation analysis can generate microvascular images with high quality and monitor spatiotemporal dynamics of blood flow with simple computation and fast processing before and after localized ischemia induced by arterial occlusion.     

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label‐free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep‐learning algorithm for classifying HCC differentiation to produce an innovative computer‐aided diagnostic method. Convolutional neural networks based on the VGG‐16 framework were trained using 217 combined two‐photon excitation fluorescence and second‐harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label‐free, automated methods for various tissues, diseases and other related classification problems.      

Insulin induces phosphorylation of the AMPA receptor subunit GluR1, reversed by ZIP,and over‐expression of Protein Kinase M zeta,reversed by amyloid beta

Insulin receptor (IR) in the brain plays a role in synaptic plasticity and cognitive functions. Phosphorylation of α‐amino‐3‐hydroxy‐5‐methylisoxazole‐4‐propionic acid (AMPA) receptors GluR1 subunit at Serine 831 is regulated by calcium–calmodulin‐dependent protein kinase II and protein kinase C that underlie long‐term potentiation and learning/memory. Recent studies have shown that the novel Protein Kinase M zeta (PKMζ) underlies synaptic plasticity and may regulate AMPAr. In this study, we show that insulin induces phosphorylation of Serine 831 GluR1 subunit of AMPAr and induces over‐expression of PKMζ; pre‐treatment with either the IR inhibitor 3‐Bromo‐5‐t‐butyl‐4‐hydroxy‐benzylidenemalonitrile (AG1024) or PKMζ inhibitor protein kinase C zeta pseudo‐substrate inhibitor returned the phosphorylation value of GluR1 to control level. Amyloid beta (Aβ) peptide in the form of oligomers interferes with IR signaling. Pre‐treating neuronal cultures with Aβ following incubation with insulin, we found a reduction of insulin‐dependent PKMζ over‐expression and MAPK/Erk (1/2) phosphorylation, i.e., signaling pathways involved in synaptic plasticity and learning/memory. These results indicate a new intracellular insulin signaling pathway, and, additionally, that insulin resistance in Alzheimer's disease is a response to the production and accumulation of Aβ.

     

Fastigial nucleus stimulation regulates neuroprotection via induction of a novel microRNA,rno‐miR‐676‐1, in middle cerebral artery occlusion rats

Previous studies have shown that fastigial nucleus stimulation (FNS) reduces tissue damage resulting from focal cerebral ischemia. Although the mechanisms of neuroprotection induced by FNS are not entirely understood, important data have been presented in the past two decades. MicroRNAs (miRNAs) are a newly discovered group of non‐coding small RNA molecules that negatively regulate target gene expression and are involved in the regulation of cell proliferation and cell apoptosis. To date, no studies have demonstrated whether miRNAs can serve as mediators of the brain's response to FNS, which leads to endogenous neuroprotection. Therefore, this study investigated the profiles of FNS‐mediated miRNAs. Using a combination of deep sequencing and microarray with computational analysis, we identified a novel miRNA in the rat ischemic cortex after 1 h of FNS. This novel miRNA (PC‐3p‐3469_406), herein referred to as rno‐miR‐676‐1, was upregulated in rats with cerebral ischemia after FNS. In vivo observations indicate that this novel miRNA may have antiapoptotic effects and contribute to neuroprotection induced by FNS. Our study provides a better understanding of neuroprotection induced by FNS.

     

Bayesian Bootstrap Clones for Censored Markov Chains

In this article, we consider r observations from a non‐homogeneous censored Markov chain, with transition probability matrix P. For the product estimator of P proposed by Aalen and Johansen (1978) and Phelan (1988), we investigate the behavior of Bayesian bootstrap clones to approximate the sampling distribution of , and then construct approximate confidence interval. It is shown that the approximation based on the random‐weighted distribution is first‐order consistent. The performance of the Bayesian bootstrap clones (BBC) is also discussed by small sample simulation. Finally, we illustrate the BBC procedure in the application to the WHO malaria survey data (cf. Singer and Cohen 1970).     

Front Cover: Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains (J. Biophotonics 3/2019)

Lens‐free holographic on‐chip microscopy holds tremendous potential to be used for the high‐throughput imaging of pathology slides for clinical diagnosis. However, due to the use of narrowband illumination sources, it is challenging for lens‐free microscopy to achieve a good color accuracy. As a solution to this challenge, an absorbance spectrum estimation‐based colorization (ASEC) method is presented which achieves accurate‐color imaging of pathology slides using only a few (e.g., three) wavelengths. Further details can be found in the article by Yibo Zhang, Tairan Liu, Yujia Huang, et al. ( e201800335 ).

     

Deep-learning-based motion correction in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this article, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts.     

Inside Front Cover: Widefield multifrequency fluorescence lifetime imaging using a two‐tap complementary metal‐oxide semiconductor camera with lateral electric field charge modulators (J. Biophotonics 5/2019)

A novel Widefield frequency‐domain fluorescence lifetime imaging system based on a ultrafast sCMOS sensor is reported. It can do parallel multi‐frequency FLIM up to 620MHz in one measurement with 64 phase images. With this system, measurement of FRET efficiency at multi‐frequencies is demonstrated in living cells. Meanwhile, the temperature change of living cells can be measured at each pixel based on FLIM of Rhodamine B at different frequencies. Further details can be found in the article by Hongtao Chen, Ning Ma, Keiichiro Kagawa, et al. ( e201800223 ).

     

Automated quantitative cytological analysis using portable microfluidic microscopy

In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide‐based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in‐suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample.

     

Single molecule localization deep within thick cells; a novel super‐resolution microscope

A novel 3D imaging system based on single‐molecule localization microscopy is presented to allow high‐accuracy drift‐free (<0.7 nm lateral; 2.5 nm axial) imaging many microns deep into a cell. When imaging deep within the cell, distortions of the point‐spread function result in an inaccurate and very compressed Z distribution. For the system to accurately represent the position of each blink, a series of depth‐dependent calibrations are required. The system and its allied methodology are applied to image the ryanodine receptor in the cardiac myocyte. Using the depth‐dependent calibration, the receptors deep within the cell are spread over a Z range that is many hundreds of nanometers greater than implied by conventional analysis. We implemented a time domain filter to detect overlapping blinks that were not filtered by a stringent goodness of fit criterion. This filter enabled us to resolve the structure of the individual (30 nm square) receptors giving a result similar to that obtained with electron tomography.

High‐accuracy deep imaging of the ryanodine receptor in the cardiac myocyte, using single‐molecule localization microscopy. Depth‐dependent calibrations are performed for accurate depth localization. The optical design featuring two independent and variable focal planes allows real‐time feedback for drift‐free deep imaging.