Cross‐scanning optical coherence tomography angiography for eye motion correction

We propose a cross‐scanning optical coherence tomography (CS‐OCT) system to correct eye motion artifacts in OCT angiography images. This system employs a dual‐illumination configuration with two orthogonally polarized beams, each of which simultaneously perform raster scanning in perpendicular direction with each other over the same area. In the reference arm, a polarization delay unit is used to acquire the two orthogonally polarized interferograms with a single photo detector by introducing different optical delay lines. The two cross‐scanned volume data are affected by the same eye motion but in two orthogonal directions. We developed a motion correction algorithm, which removes artifacts in the slow axis of each angiogram using the other and merges them through a nonrigid registration algorithm. In this manner, we obtained a motion‐corrected angiogram within a single volume scanning time without additional eye‐tracking devices.     

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.     

Contrast‐enhanced optical coherence tomography for melanoma detection: An in vitro study

Optical coherence tomography (OCT), with a high‐spatial resolution (<10 microns), intermediate penetration depth (~1.5 mm) and volumetric imaging capability is a great candidate to be used as a diagnostic‐assistant modality in dermatology. At this time, the accuracy of OCT for melanoma detection is lower than anticipated. In this letter, we studied for the first time, the use of a novel contrast agent consist of ultra‐small nanoparticles conjugated to a melanoma biomarker to improve the accuracy of OCT for differentiation of melanoma cells from nonmelanoma cells, in vitro. We call this approach SMall nanoparticle Aggregation‐enhanced Radiomics of Tumor (SMART)‐OCT imaging. This initial proof of concept study is the first step toward the broad utilization of this method for high accuracy all types of tumor detection applications.     

General model for depth‐resolved estimation of the optical attenuation coefficients in optical coherence tomography

We present the proof of concept of a general model that uses the tissue sample transmittance as input to estimate the depth‐resolved attenuation coefficient of tissue samples using optical coherence tomography (OCT). This method allows us to obtain an image of tissue optical properties instead of intensity contrast, guiding diagnosis and tissues differentiation, extending its application from thick to thin samples. The performance of our method was simulated and tested with the assistance of a home built single‐layered and multilayered phantoms (~100 μm each layer) with known attenuation coefficient on the range of 0.9 to 2.32 mm^?1. It is shown that the estimated depth‐resolved attenuation coefficient recovers the reference values, measured by using an integrating sphere followed by the inverse adding doubling processing technique. That was corroborated for all situations when the correct transmittance value is used with an average difference of 7%. Finally, we applied the proposed method to estimate the depth‐resolved attenuation coefficient for a thin biological sample, demonstrating the ability of our method on real OCT images.     

Orthogonal-polarization-gating optical coherence tomography for human sweat ducts in vivo

We propose an orthogonal-polarization-gating optical coherence tomography (OPG-OCT) for human sweat ducts in vivo. OPG-OCT is composed of the orthogonal linearly polarized light of a sample arm individually interfering with orthogonal linearly polarized lights of the reference arms, where OPG-OCT induces two images, one reflecting the projection intensity and the other the horizontal linear diattenuation (HLD). The results demonstrate that OPG-OCT projection intensity could improve the image quality of sweat ducts. HLD also clearly illustrates the spiral shape of the sweat ducts. Finally, sweat ducts in intensity image are segmented by employing convolutional neural networks (CNN). The proportions of left-handed and right-handed ducts are extracted to characterize the sweat ducts based on HLD. Therefore, the OPG-OCT technique employing CNN for the human sweat glands has the potential to automatically identify the human sweat ducts in vivo.     

Quantifying dental biofilm growth using cross-polarization optical coherence tomography

Aims: Quantifying the ex vivo growth of complex multispecies dental biofilms using cross‐polarization 1310‐nm optical coherence tomography (CP‐OCT) system was investigated. Methods and Results: Bacterial microcosms, which were derived from plaque samples of paediatric subjects, were incubated in a biofilm reactor system containing discs of different dental materials for 72 h with daily sucrose pulsing (5×). CP‐OCT analysis of biofilm mass was validated with crystal violet (CV) assays at various growth stages of these complex biofilms. CP‐OCT was able to filter out the back‐reflected signals of water layers in the hydrated biofilm and allowed for direct biofilm quantification. The overall depth‐resolved scattering intensity of the biofilm showed very strong positive correlation with CV assay quantification (Spearman’s ρ = 0·92) during the growth phase of the biofilm. Conclusion: CP‐OCT was able to quantify the mass of the biofilm by measuring the overall depth‐resolved scattering of the biofilm. Significance and Impact of the Study: CP‐OCT has the ability to nondestructively monitor biofilm growth and elucidate the growth characteristics of these microcosms on different dental material compositions.     

Characterization of the human myocardium by optical coherence tomography

Imaging of cardiac tissue structure plays a critical role in the treatment and understanding of cardiovascular disease. Optical coherence tomography (OCT) offers the potential to provide valuable, high‐resolution imaging of cardiac tissue. However, there is a lack of comprehensive OCT imaging data of the human heart, which could improve identification of structural substrates underlying cardiac abnormalities. The objective of this study was to provide qualitative and quantitative analysis of OCT image features throughout the human heart. Fifty human hearts were acquired, and tissues from all chambers were imaged with OCT. Histology was obtained to verify tissue composition. Statistical differences between OCT image features corresponding to different tissue types and chambers were estimated using analysis of variance. OCT imaging provided features that were able to distinguish structures such as thickened collagen, as well as adipose tissue and fibrotic myocardium. Statistically significant differences were found between atria and ventricles in attenuation coefficient, and between adipose and all other tissue types. This study provides an overview of OCT image features throughout the human heart, which can be used for guiding future applications such as OCT‐integrated catheter‐based treatments or ex vivo investigation of structural substrates.     

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.     

Comparative study of optical coherence tomography angiography algorithms for rodent retinal imaging

Optical coherence tomography angiography (OCTA) is a functional extension of optical coherence tomography for non-invasive in vivo three-dimensional imaging of the microvasculature of biological tissues. Several algorithms have been developed to construct OCTA images from the measured optical coherence tomography signals. In this study, we compared the performance of three OCTA algorithms that are based on the variance of phase, amplitude, and the complex representations of the optical coherence tomography signals for rodent retinal imaging, namely the phase variance, improved speckle contrast, and optical microangiography. The performance of the different algorithms was evaluated by comparing the quality of the OCTA images regarding how well the vasculature network can be resolved. Quantities that are widely used in ophthalmic studies including blood vessel density, vessel diameter index, vessel perimeter index, vessel complexity index were also compared. Results showed that both the improved speckle contrast and optical microangiography algorithms are more robust than phase variance, and they can reveal similar vasculature features while there are statistical differences in the calculated quantities.     

Cross-domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Deep learning based retinopathy classification with optical coherence tomography (OCT) images has recently attracted great attention. However, existing deep learning methods fail to work well when training and testing datasets are different due to the general issue of domain shift between datasets caused by different collection devices, subjects, imaging parameters, etc. To address this practical and challenging issue, we propose a novel deep domain adaptation (DDA) method to train a model on a labeled dataset and adapt it to an unlabelled dataset (collected under different conditions). It consists of two modules for domain alignment, that is, adversarial learning and entropy minimization. We conduct extensive experiments on three public datasets to evaluate the performance of the proposed method. The results indicate that there are large domain shifts between datasets, resulting a poor performance for conventional deep learning methods. The proposed DDA method can significantly outperform existing methods for retinopathy classification with OCT images. It achieves retinopathy classification accuracies of 0.915, 0.959 and 0.990 under three cross-domain (cross-dataset) scenarios. Moreover, it obtains a comparable performance with human experts on a dataset where no labeled data in this dataset have been used to train the proposed DDA method. We have also visualized the learnt features by using the t-distributed stochastic neighbor embedding (t-SNE) technique. The results demonstrate that the proposed method can learn discriminative features for retinopathy classification.     

Miniaturized all fiber probe for optical coherence tomography and pH detection of biological tissue

We present a novel all-fiber probe with 710-μm outside diameter for combined optical coherence tomography and pH detection. In cancer surgery, a significant challenge is how to completely remove the malignant tumor without cutting too much normal tissue. The difference between cancer tissue and normal tissue not only lies in morphology and structure but also in tissue pH, where malignant tissue has a lower pH. This dual-modality probe combined optical coherence tomography and pH detection of biological tissue, is expected to determine whether the tissue is cancerous quickly and accurately. The probe utilizes a typical three-segment structure (double-clad fiber - no-core fiber - graded-index fiber). We obtained a lateral resolution of ~10.6 μm, a working distance of ~506 μm and a pH measurement accuracy of 0.01 pH unit for the probe. The performance of the all-fiber probe was verified through an ex vivo experiment using the porcine brain specimen.     

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.     

Quantitative assessment of optical coherence tomography angiography algorithms for neuroimaging

Optical coherence tomography (OCT) angiography can noninvasively map microvascular networks and quantify blood flow in a cerebral cortex with a resolution of 1 to 10 μm and a penetration depth of 2 to 3 mm incorporating OCT signals and angiography algorithms. Different angiography algorithms have been developed in recent years; however, the performance of the algorithms has not been assessed quantitatively for neuroimaging applications. In this paper, we developed four metrics including vascular connectivity, contrast‐to‐noise ratio, signal‐to‐noise ratio and processing time to quantitatively assess the performance of OCT angiography algorithms in image quality and computation speed. After the imaging of a rat cortex using an OCT system, the cerebral microvascular networks were visualized by seven algorithms, and the performance of the algorithms was quantified and compared. Quantitative performance assessment of the algorithms can provide suggestions for the selection of appropriate OCT angiography algorithms in neuroimaging.     

In vivo monitoring of thrombosis in mice by optical coherence tomography

The objective of this study is to establish a novel method for continuously monitoring thrombus progression with various outcome measures and to assess the efficacy of antithrombotic drugs in murine thrombosis model in mice. In the study, thrombus was induced in the femoral vein of mice by FeCl₃ and monitored over time by spectral‐domain optical coherence tomography (OCT). Three‐dimensional images of thrombi with or without heparin as an antithrombotic agent were obtained from OCT angiography. In addition, several parameters of thrombi were analyzed and compared between control and anticoagulant groups. By using OCT, we were able to trace thrombus generation in the same mouse in real time. We found that in our model heparin reduced thrombus size by ~60% and thrombus cross‐sectional area by 50%. OCT results also show that both time to thrombus size (>0.02mm³) and time to occlusion (>30%) were significantly reduced after heparin addition. This study demonstrates that OCT reliably monitors thrombus generation and progression from various aspects including thrombus size. This enables us to measure the kinetic of thrombosis more accurately, and effectively evaluate the efficacy and activities of antithrombotic drugs. This model may represent a useful tool in antithrombotic drug discoveries in preclinical studies.      

Closed loop tracked Doppler optical coherence tomography based heart monitor for the Drosophila melanogaster larvae

This paper presents a novel instrument for biosciences, useful for studies of moving embryos. A dual sequential imaging/measurement channel is assembled via a closed‐loop tracking architecture. The dual channel system can operate in two regimes: (i) single‐point Doppler signal monitoring or (ii) fast 3‐D swept source OCT imaging. The system is demonstrated for characterizing cardiac dynamics in Drosophila melanogaster larva. Closed loop tracking enables long term in vivo monitoring of the larvae heart without anesthetic or physical restraint. Such an instrument can be used to measure subtle variations in the cardiac behavior otherwise obscured by the larvae movements.

A fruit fly larva (top) was continuously tracked for continuous remote monitoring. A heartbeat trace of freely moving larva (bottom) was obtained by a low coherence interferometry based doppler sensing technique.     

A spatial resolution evaluation method of endoscopic optical coherence tomography system using the annular phantom

As an important biomedical imaging method, endoscopic optical coherence tomography (OCT) is necessary to check its performance regularly. The ordinary plane phantoms are only able to evaluate part of image tangent to the probe. In this research, a spatial resolution estimate method of the endoscope OCT system is proposed. The annular phantom, made by uniformly distributing golden scattered microparticles in polydimethylsiloxane (PDMS), can provide dynamic scanning imaging evaluation of endoscopic OCT system, closer to its actual working status. The point spread function analysis method is used to analyze the imaging results of the annular phantom with the endoscopic OCT system. And many scattered particles are statistically analyzed to determine the spatial resolution of the endoscope OCT system. The method is low in cost, simple and convenient. It is valuable for the development of test standards for endoscope OCT systems.     

Imaging atherosclerotic plaque composition with intracoronary optical coherence tomography

Optical coherence tomography (OCT) allows highly accurate diagnosis of atherosclerotic plaques, including measurement of the thickness of fibrous caps, permitting an assessment of the risk of rupture. While the OCT image presents morphological information in highly resolved detail, it relies on interpretation by trained readers for the identification of tissue type. We developed a method for quantitative classification of atherosclerotic plaque constituents. The optical attenuation coefficient μ_t distinguishes different tissue types: necrotic core and macrophage infiltration exhibit strong attenuation, μ_t≥10 mm⁻¹, while calcific and fibrous tissue have a lower μ_t≈2–5 mm⁻¹. (Neth Heart J 2009;17:448-50.)     

Preclinical mouse model of optical coherence tomography for subcortical brain imaging without dissection

The purpose of this study was to investigate the feasibility of using optical coherence tomography (OCT) to identify internal brain lesions, specifically intracerebral hemorrhage, without dissection. Mice with artificially injected brain hematomas were used to test the OCT system, and the recorded images were compared with microscopic images of the same mouse brains after hematoxylin and eosin staining. The intracranial structures surrounding the hematomas were clearly visualized by the OCT system without dissection. These images reflect the ability of OCT to determine the extent of a lesion in several planes. OCT is a useful technology, and these findings could be used as a starting point for future research in intraoperative imaging.     

Assessment of simulated osteoporosis in alveolar bone using optical coherence tomography

Steoporosis is a skeletal disorder that compromises bone resistance and its diagnosis is usually performed using dual energy X‐ray absorptiometry. Thus, the search for efficient diagnostic methods that do not involve the emission of ionizing radiation is necessary. This study proposed to use the Optical Coherence Tomography (OCT) to evaluate osteoporosis in alveolar bone. Osteoporosis lesions is simulated in vitro in porcine bones, and imaging is performed by OCT and micro‐computed tomography (micro‐CT). A developed algorithm is proposed to calculate the optical attenuation coefficient ( μ _t), mean optical attenuation coefficient (), integrated reflectivity (ΔR) and bone density ( BD). The , ΔR and BD parameters shows a good correlation to micro‐CT parameters (bone volume/tissue volume and total porosity). The μ _t and methods are negatively impacted by non‐uniform intensities distribution in osteoporosis images. In conclusion, BD and ΔR analysis demonstrates to be potential techniques for diagnosis and monitoring of osteoporosis using OCT.      

Retinal cross-section motion correction in three-dimensional retinal optical coherence tomography

Motion correction is an important issue in ophthalmic optical coherence tomography (OCT), and can improve the ability of data sets to reflect the physiological structures of tissues and make visualization and subsequent analysis easier. In this study, we present a novel method to correct the cross-sectional motion artifacts in retinal OCT volumes. Motion along the x-direction (fast-scan direction) is corrected through the normalized cross-correlation algorithm, while axial motion compensation is performed using the polynomial fitting method on the inner segment/outer segment (IS/OS) layer segmented by the shortest path faster algorithm (SPFA). The results of volunteers with central serous chorioretinopathy demonstrate that the proposed method effectively corrects motion artifacts in OCT volumes and may have potential application value in the evaluation of ophthalmic diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration.