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.     

A feasibility study of OCT for anatomical and vascular phenotyping of mouse embryo

The embryo phenotyping of genetic murine model is invaluable when investigating functions of genes underlying embryonic development and birth defect. Although traditional imaging technologies such as ultrasound are very useful for evaluating phenotype of murine embryos, the use of advanced techniques for phenotyping is desirable to obtain more information from genetic research. This letter tests the feasibility of optical coherence tomography (OCT) as a high‐throughput phenotyping tool for murine embryos. Three‐dimensional OCT imaging is performed for live and cleared mouse embryos in the late developmental stage (embryonic day 17.5). By using a dynamic focusing method and OCT angiography (OCTA) approach, our OCT imaging of the embryo exhibits rapid and clean visualization of organ structures deeper than 5 mm and complex microvasculature of perfused blood vessels in the murine embryonic body. This demonstration suggests that OCT imaging can be useful for comprehensively assessing embryo anatomy and angiography of genetically engineered mice.     

Inside Cover

Optical Coherence Tomography angiography (OCTA) is widely used to image chorioretinal vasculature, with contrast that derives from scattering and motion of red blood cells (RBCs). In the rat eye, a scattering tracer highlights vertical vessels which are not visualized by intrinsic RBC scattering alone. The dependence of microvessel OCTA on angular orientation, explored in this work, represents a potential artifact that should be considered in the clinical interpretation of OCTA. Further details can be found in the article by Jun Zhu, Marcel T. Bernucci, Conrad W. Merkle, and Vivek J. Srinivasan ( e202000090) .

     

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.     

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.     

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.     

Vascular changes precede tomographic changes in diabetic eyes without retinopathy and improve artificial intelligence diagnostics

The purpose of this study was to evaluate early vascular and tomographic changes in the retina of diabetic patients using artificial intelligence (AI). The study included 74 age‐matched normal eyes, 171 diabetic eyes without retinopathy (DWR) eyes and 69 mild non‐proliferative diabetic retinopathy (NPDR) eyes. All patients underwent optical coherence tomography angiography (OCTA) imaging. Tomographic features (thickness and volume) were derived from the OCTA B‐scans. These features were used in AI models. Both OCT and OCTA features showed significant differences between the groups (P < .05). However, the OCTA features indicated early retinal changes in DWR eyes better than OCT (P < .05). In the AI model using both OCT and OCTA features simultaneously, the best area under the curve of 0.91 ± 0.02 was obtained (P < .05). Thus, the combined use of AI, OCT and OCTA significantly improved the early diagnosis of diabetic changes in the retina.     

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.     

Noninvasive multimodal imaging by integrating optical coherence tomography with autofluorescence imaging for dental applications

We report the development of an integrated multifunctional imaging system capable of providing anatomical (optical coherence tomography, OCT), functional (OCT angiography, OCTA) and molecular imaging (light‐induced autofluorescence, LIAF) for in vivo dental applications. Blue excitation light (405 nm) was used for LIAF imaging, while the OCT was powered by a 1310 nm swept laser source. A red‐green‐blue digital camera, with a 450 nm cut‐on broadband optical filter, was used for LIAF detection. The exciting light source and camera were integrated directly with the OCT scanning probe. The integrated system used two noninvasive imaging modalities to improve the speed of in vivo OCT data collection and to better target the regions of interest. The newly designed system maintained the ability to detect differences between healthy and hypomineralized teeth, identify dental biofilm and visualize the microvasculature of gingival tissue. The development of the integrated OCT‐LIAF system provides an opportunity to conduct clinical studies more efficiently, examining changes in oral conditions over time.     

High resolution,label‐free photoacoustic imaging of live chicken embryo developing in bioengineered eggshell

Chicken embryos have been proven to be an attractive vertebrate model for biomedical research. They have helped in making significant contributions for advancements in various fields like developmental biology, cancer research and cardiovascular studies. However, a non‐invasive, label‐free method of imaging live chicken embryo at high resolution still needs to be developed and optimized. In this work, we have shown the potential of photoacoustic tomography (PAT) for imaging live chicken embryos cultured in bioengineered eggshells. Laser pulses at wavelengths of 532 and 740 nm were used for attaining cross‐sectional images of chicken embryos at different developmental stages. Cross‐sections along different depths were imaged to gain knowledge of the relative depth of different vessels and organs. Due to high optical absorption of vasculature and embryonic eye, images with good optical contrast could be acquired using this method. We have thus reported a label‐free method of performing cross‐sectional imaging of chicken embryos at high resolution demonstrating the capacity of PAT as a promising tool for avian embryo imaging.     

Front Cover: Photoacoustic imaging of the human placental vasculature (J. Biophotonics 4/2020)

Successful therapy of twin‐to‐twin transfusion syndrome requires accurate imaging to guide laser photocoagulation of the anastomosing placental vessels. Photoacoustic (PA) imaging is an alternative imaging method that provides contrast for hemoglobin, and in this study, it was used to visualize chorionic superficial and subsurface vasculature in human placentas. The strong potential of PA imaging to guide minimally invasive fetal therapies was demonstrated. Further details can be found in the article by Efthymios Maneas, Rosalind Aughwane, Nam Huynh, et al. ( e201900167 ).

     

Quantification of diabetic macular ischemia using novel three‐dimensional optical coherence tomography angiography metrics

We applied three‐dimensional (3D) analysis to optical coherence tomography angiography (OCTA) to measure macular ischemia in eyes affected by non‐proliferative diabetic retinopathy (DR). A previously validated algorithm was applied to OCTA data in order to obtain 3D visualization of the retinal vasculature. Successively, a global thresholding algorithm was applied and two novel quantitative metrics were introduced: 3D vascular volume and 3D perfusion density. Two‐dimensional (2D) OCTA metrics were also obtained with different binarization thresholds for comparison. Of the 30 patients included, 15 were diagnosed with DR and 15 were controls. The 3D vascular volume and 3D perfusion density were reduced in DR eyes (P < .0001). The 2D variables also significantly differ between groups. The 3D perfusion density had the highest area under the receiver operating characteristic curve (0.964) among tested variables. Assessing quantitative perfusion using 3D analysis is reliable and promising, and with an elevated diagnostic efficacy in identifying DR eyes.     

Portable deep learning singlet microscope

Having the least lenses, the significant feature of the singlet imaging system, helps the development of the portable and cost‐effective microscopes. A novel method of monochromatic/color singlet microscopy, which is combined with only one aspheric lens and deep learning computational imaging technology, is proposed in this article. The designed singlet aspheric lens is an approximate linear signal system, which means modulation‐transfer‐function curves on all field‐of‐views (5 mm diagonally) are almost coincident with each other. The purpose of the designed linear signal system is to further improve the resolution of our microscope by using deep learning algorithm. As a proof of concept, we designed a singlet microscopy based on our method, which weighs only 400 g. The experimental data and results of the sample USAF?1951 target and bio‐sample (the Equisetum‐arvense Strobile L.S), prove that the performance of the proposed singlet microscope is competitive to a commercial microscope with the 4X/NA0.1 objective lens. We believe that our idea and method would guide to design more cost‐effective and powerful singlet imaging system.     

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.      

A novel automatic 3D stitching algorithm for optical coherence tomography angiography and its application in dermatology

Optical coherence tomography (OCT) and OCT angiography (OCTA) techniques offer numerous advantages in clinical skin applications but the field of view (FOV) of current commercial systems are relatively limited to cover the entire skin lesion. The typical method to expand the FOV is to apply wide field objective lens. However, lateral resolution is often sacrificed when scanning with these lenses. To overcome this drawback, we developed an automated 3D stitching method for creating high-resolution skin structure and vascular volumes with large field of view, which was realized by montaging multiple adjacent OCT and OCTA volumes. The proposed stitching method is demonstrated by montaging 3 × 3 OCT and OCTA volumes (nine OCT/OCTA volumes as one data set with each volume covers 2.5 cm × 2.5 cm area) of healthy thin and thick skin from six volunteers. The proposed stitching protocol achieves high flexibility and repeatable for all the participants. Moreover, according to evaluation of structural similarity index and feature similarity index, our proposed stitched result has a superior similarity to single scanning protocol in large-scaled. We had also verified its improved performance through assessing metrics of vessel contrast-noise-ratio (CNR) from 2.07 ± 0.44 (single large-scaled scanning protocol) to 3.05 ± 0.51 (proposed 3 × 3 sub-volume stitching method).     

Three‐dimensional mapping of the attenuation coefficient in optical coherence tomography to enhance breast tissue microarchitecture contrast

Effective intraoperative tumor margin assessment is needed to reduce re‐excision rates in breast‐conserving surgery (BCS). Mapping the attenuation coefficient in optical coherence tomography (OCT) throughout a sample to create an image (attenuation imaging) is one promising approach. For the first time, three‐dimensional OCT attenuation imaging of human breast tissue microarchitecture using a wide‐field (up to ~45 × 45 × 3.5 mm) imaging system is demonstrated. Representative results from three mastectomy and one BCS specimen (from 31 specimens) are presented with co‐registered postoperative histology. Attenuation imaging is shown to provide substantially improved contrast over OCT, delineating nuanced features within tumors (including necrosis and variations in tumor cell density and growth patterns) and benign features (such as sclerosing adenosis). Additionally, quantitative micro‐elastography (QME) images presented alongside OCT and attenuation images show that these techniques provide complementary contrast, suggesting that multimodal imaging could increase tissue identification accuracy and potentially improve tumor margin assessment.     

Optimal imaging windows of indocyanine green‐assisted near‐infrared dental imaging with rat model and its comparison to X‐ray imaging

In this study, we used rat animal model to compare the efficiency of indocyanine green (ICG)‐assisted dental near‐infrared fluorescence imaging with X‐ray imaging, and we optimized the imaging window for both unerupted and erupted molars. The results show that the morphology of the dental structures was observed clearly from ICG‐assisted dental images (especially through the endoscope). A better image contrast was easily acquired at the short imaging windows (<10 minutes) for unerupted and erupted molars. For unerupted molars, there is another optimized imaging window (48‐96 hours) with a prominent glow‐in‐the‐dark effect: only the molars remain bright. This study also revealed that the laser ablation of dental follicles can disrupt the molar development, and our method is able to efficiently detect laser‐treated molars and acquire the precise morphology. Thus, ICG‐assisted dental imaging has the potential to be a safer and more efficient imaging modality for the real‐time diagnosis of dental diseases.     

Imaging photoplethysmography and videocapillaroscopy enable noninvasive study of zebrafish cardiovascular system functioning

We report on the noninvasive method for in vivo study of fish's cardiovascular system, that is, the heart and the structure of vessels that carry blood throughout the body. The proposed approach is based on combined photoplethysmographic and videocapillaroscopic microscopic imaging and enables noncontact two‐dimensional mapping of blood volume changes. We demonstrate that the obtained data allows precise measurements of heartbeat, blood flow velocity and other important parameters (see Videos S1 and S2 ). To validate the developed image processing technique, we have carried out multiple experiments on zebrafish—a well‐proven informative model organism widely used to understand cardiac development. The proposed approach may be effective for the study of cardiovascular system formation and functioning as well as the impact of various influencing factors on them.     

Deep learning protocol for improved photoacoustic brain imaging

One of the key limitations for the clinical translation of photoacoustic imaging is penetration depth that is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a method based on deep learning to virtually increase the MPE in order to enhance the signal‐to‐noise ratio of deep structures in the brain tissue. The proposed method is evaluated in an in vivo sheep brain imaging experiment. We believe this method can facilitate clinical translation of photoacoustic technique in brain imaging, especially in transfontanelle brain imaging in neonates.