Showing papers on "Optical coherence tomography published in 2017"
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Abstract: Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.
440 citations
TL;DR: Deep learning techniques achieve high accuracy and is effective as a new image classification technique in Optical coherence tomography and have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.
388 citations
20 Nov 2017
TL;DR: In this paper, a deep neural network was used to improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. But, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design.
Abstract: We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging.
377 citations
TL;DR: A convolutional neural network (CNN) is developed that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians and can be trained to perform automated segmentations of clinically relevant image features.
Abstract: Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations of clinically relevant image features.
275 citations
TL;DR: Polarization sensitive (PS) OCT draws advantage from the fact that several materials and tissues can change the light's polarization state, adding an additional contrast channel and providing quantitative information.
Abstract: Optical coherence tomography (OCT) is now a well-established modality for high-resolution cross-sectional and three-dimensional imaging of transparent and translucent samples and tissues. Conventional, intensity based OCT, however, does not provide a tissue-specific contrast, causing an ambiguity with image interpretation in several cases. Polarization sensitive (PS) OCT draws advantage from the fact that several materials and tissues can change the light's polarization state, adding an additional contrast channel and providing quantitative information. In this paper, we review basic and advanced methods of PS-OCT and demonstrate its use in selected biomedical applications.
269 citations
TL;DR: The approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability and identifies salient responses during prediction to understand learned filter characteristics.
Abstract: We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.
200 citations
TL;DR: In this article, the authors compare the performance of HSI and Fourier single-pixel imaging with theoretical analysis and experiments, and show that FSI is more efficient than HSI while HSI was more noise-robust than FSI.
Abstract: Single-pixel imaging which employs active illumination to acquire spatial information is an innovative imaging scheme and has received increasing attentions in recent years. It is applicable to imaging at non-visible wavelengths and imaging under low light conditions. However, single-pixel imaging has once encountered problems of low reconstruction quality and long data-acquisition time. Hadamard single-pixel imaging (HSI) and Fourier single-pixel imaging (FSI) are two representative deterministic model based techniques. Both techniques are able to achieve high-quality and efficient imaging, remarkably improving the applicability of single-pixel imaging scheme. In this paper, we compare the performances of HSI and FSI with theoretical analysis and experiments. The results show that FSI is more efficient than HSI while HSI is more noise-robust than FSI. Our work may provide a guideline for researchers to choose suitable single-pixel imaging technique for their applications.
176 citations
TL;DR: The historical evolution and current state of the art of high-speed OCT systems, with focus on wavelength swept light sources and swept source OCT systems are discussed.
Abstract: Imaging speed is one of the most important parameters that define the performance of optical coherence tomography (OCT) systems. During the last two decades, OCT speed has increased by over three orders of magnitude. New developments in wavelength-swept lasers have repeatedly been crucial for this development. In this review, we discuss the historical evolution and current state of the art of high-speed OCT systems, with focus on wavelength swept light sources and swept source OCT systems.
172 citations
TL;DR: This review paper points out the key aspects of the physics and the technology that has enabled a more than 2 orders of magnitude increase in sensitivity, and as a consequence an increase in the imaging speed without loss of image quality.
Abstract: Optical coherence tomography (OCT) has become one of the most successful optical technologies implemented in medicine and clinical practice mostly due to the possibility of non-invasive and non-contact imaging by detecting back-scattered light. OCT has gone through a tremendous development over the past 25 years. From its initial inception in 1991 [Science254, 1178 (1991)] it has become an indispensable medical imaging technology in ophthalmology. Also in fields like cardiology and gastro-enterology the technology is envisioned to become a standard of care. A key contributor to the success of OCT has been the sensitivity and speed advantage offered by Fourier domain OCT. In this review paper the development of FD-OCT will be revisited, providing a single comprehensive framework to derive the sensitivity advantage of both SD- and SS-OCT. We point out the key aspects of the physics and the technology that has enabled a more than 2 orders of magnitude increase in sensitivity, and as a consequence an increase in the imaging speed without loss of image quality. This speed increase provided a paradigm shift from point sampling to comprehensive 3D in vivo imaging, whose clinical impact is still actively explored by a large number of researchers worldwide.
168 citations
TL;DR: The physical processes underlying tissue mechanical response based on static and dynamic displacement methods and the assumptions commonly used to interpret displacement and strain measurements in terms of tissue elasticity for static OCE and propagating wave modes in dynamic OCE are described.
Abstract: Optical coherence elastography (OCE) can provide clinically valuable information based on local measurements of tissue stiffness. Improved light sources and scanning methods in optical coherence tomography (OCT) have led to rapid growth in systems for high-resolution, quantitative elastography using imaged displacements and strains within soft tissue to infer local mechanical properties. We describe in some detail the physical processes underlying tissue mechanical response based on static and dynamic displacement methods. Namely, the assumptions commonly used to interpret displacement and strain measurements in terms of tissue elasticity for static OCE and propagating wave modes in dynamic OCE are discussed with the ultimate focus on OCT system design for ophthalmic applications. Practical OCT motion-tracking methods used to map tissue elasticity are also presented to fully describe technical developments in OCE, particularly noting those focused on the anterior segment of the eye. Clinical issues and future directions are discussed in the hope that OCE techniques will rapidly move forward to translational studies and clinical applications.
153 citations
TL;DR: In vivo imaging of superficial microvasculature and melanoma tumors was demonstrated with ~2.7±0.5 μm lateral resolution and Phantom studies confirmed signal dependence on optical absorption, index contrast and excitation fluence.
Abstract: Elasto-optical refractive index modulation due to photoacoustic initial pressure transients produced significant reflection of a probe beam when the absorbing interface had an appreciable refractive index difference This effect was harnessed in a new form of non-contact optical resolution photoacoustic microscopy called photoacoustic remote sensing microscopy A non-interferometric system architecture with a low-coherence probe beam precludes detection of surface oscillations and other phase-modulation phenomenon The probe beam was confocal with a scanned excitation beam to ensure detection of initial pressure-induced intensity reflections at the subsurface origin where pressures are largest Phantom studies confirmed signal dependence on optical absorption, index contrast and excitation fluence In vivo imaging of superficial microvasculature and melanoma tumors was demonstrated with ~27±05 μm lateral resolution A new design for a photoacoustic microscope capable of high-quality, real-time in vivo imaging has been developed by scientists in Canada Parsin Hajireza and co-workers from the University of Alberta and the company Illumisonics report that, unlike other designs, their approach does not rely on interferometric detection of photoacoustic stress, which can be problematic Instead, it involves making time-varying intensity measurements of the reflection of a probe beam from the sample A high signal-to-noise ratio and a working distance of 25 centimetres between the sample and the system's objective lens are achievable The researchers demonstrate the potential of their scheme for biomedical applications by using to perform in vivo imaging of microvasculature and melanoma tumours in chicken embryos with a spatial resolution of 27 micrometres
TL;DR: The use of OCTA is explored in iris vessel dilation seen in various forms of iritis, as a predictive factor for further episodes of inflammation, with an emphasis on monitoring progression and response to treatment, as well as predicting visual complications.
TL;DR: Adaptive Optic Coherence tomography (AO-OCT) as discussed by the authors is a method that combines adaptive optics and optical coherence to obtain volumetric retinal imaging with high isotropic resolution.
Abstract: In vivo imaging of the human retina with a resolution that allows visualization of cellular structures has proven to be essential to broaden our knowledge about the physiology of this precious and very complex neural tissue that enables the first steps in vision. Many pathologic changes originate from functional and structural alterations on a cellular scale, long before any degradation in vision can be noted. Therefore, it is important to investigate these tissues with a sufficient level of detail in order to better understand associated disease development or the effects of therapeutic intervention. Optical retinal imaging modalities rely on the optical elements of the eye itself (mainly the cornea and lens) to produce retinal images and are therefore affected by the specific arrangement of these elements and possible imperfections in curvature. Thus, aberrations are introduced to the imaging light and image quality is degraded. To compensate for these aberrations, adaptive optics (AO), a technology initially developed in astronomy, has been utilized. However, the axial sectioning provided by retinal AO-based fundus cameras and scanning laser ophthalmoscope instruments is limited to tens of micrometers because of the rather small available numerical aperture of the eye. To overcome this limitation and thus achieve much higher axial sectioning in the order of 2-5µm, AO has been combined with optical coherence tomography (OCT) into AO-OCT. This enabled for the first time in vivo volumetric retinal imaging with high isotropic resolution. This article summarizes the technical aspects of AO-OCT and provides an overview on its various implementations and some of its clinical applications. In addition, latest developments in the field, such as computational AO-OCT and wavefront sensor less AO-OCT, are covered.
TL;DR: This work presents a new pulse-stretching technique, termed free-space angular-chirp-enhanced delay (FACED), with three distinguishing features absent in the prevailing dispersive-fiber-based implementations, and demonstrates not only ultrafast laser-scanning time-stretch imaging with superior bright-field image quality compared with previous work but also, for the first time, MHz fluorescence and colorized time-Stretch microscopy.
Abstract: Optical time-stretch imaging enables the continuous capture of non-repetitive events in real time at a line-scan rate of tens of MHz—a distinct advantage for the ultrafast dynamics monitoring and high-throughput screening that are widely needed in biological microscopy. However, its potential is limited by the technical challenge of achieving significant pulse stretching (that is, high temporal dispersion) and low optical loss, which are the critical factors influencing imaging quality, in the visible spectrum demanded in many of these applications. We present a new pulse-stretching technique, termed free-space angular-chirp-enhanced delay (FACED), with three distinguishing features absent in the prevailing dispersive-fiber-based implementations: (1) it generates substantial, reconfigurable temporal dispersion in free space (>1 ns nm−1) with low intrinsic loss (<6 dB) at visible wavelengths; (2) its wavelength-invariant pulse-stretching operation introduces a new paradigm in time-stretch imaging, which can now be implemented both with and without spectral encoding; and (3) pulse stretching in FACED inherently provides an ultrafast all-optical laser-beam scanning mechanism at a line-scan rate of tens of MHz. Using FACED, we demonstrate not only ultrafast laser-scanning time-stretch imaging with superior bright-field image quality compared with previous work but also, for the first time, MHz fluorescence and colorized time-stretch microscopy. Our results show that this technique could enable a wider scope of applications in high-speed and high-throughput biological microscopy that were once out of reach. A new pulse-stretching technique has enabled ultrafast laser-scanning time-stretch imaging to be achieved in the important visible region. Optical time-stretching is used to realize real-time continuous imaging at ultrahigh frame rates, but current technologies based on dispersive fibers are generally restricted to near-infrared wavelengths. Now, a team at the University of Hong Kong led by Kevin Tsia has overcome this limitation by developing a pulse-stretching technique that they dub free-space angular-chirp-enhanced delay. It has the advantages of generating a large dispersion in free space with low loss and of enabling wavelength-invariant stretching. The researchers demonstrated its potential by realizing ultrafast laser-scanning time-stretch imaging with excellent bright-field image quality. They also used it to achieve megahertz fluorescence and color time-stretch microscopy at the optical wavelength of 700 nm.
TL;DR: A summary of the development of vis-OCT and its demonstrated applications is provided and perspectives on future technology improvement and applications are provided.
Abstract: Visible-light optical coherence tomography (vis-OCT) is an emerging imaging modality, providing new capabilities in both anatomical and functional imaging of biological tissue. It relies on visible light illumination, whereas most commercial and investigational OCTs use near-infrared light. As a result, vis-OCT requires different considerations in engineering design and implementation but brings unique potential benefits to both fundamental research and clinical care of several diseases. Here, we intend to provide a summary of the development of vis-OCT and its demonstrated applications. We also provide perspectives on future technology improvement and applications.
TL;DR: The review aims to critically discuss the use of optical coherence tomography (OCT) for the visualization of the biofilm structure as well as its dynamic behavior, and shows the importance of OCT with respect to a better description of mechanical biofilm properties.
Abstract: Imaging of biofilm systems is a prerequisite for a better understanding of both structure and its function. The review aims to critically discuss the use of optical coherence tomography (OCT) for the visualization of the biofilm structure as well as its dynamic behavior. A short overview on common and well-known, established imaging techniques for biofilms such as scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), Raman microscopy (RM), and magnetic resonance imaging (MRI) paves the way to imaging biofilms at the mesoscale, which is perfectly covered by means of OCT. Principle, resolution, imaging velocity, and limitations of OCT are subsequently presented and discussed in the context of biofilm applications. Examples are provided showing the strength of this technique with respect to the visualization of the mesoscopic biofilm structure as well as the estimation of flow profiles and shear rates. Common and new structural parameters derived from OCT datasets are presented. Additionally, the review shows the importance of OCT with respect to a better description of mechanical biofilm properties. Finally, the implementation of multi-dimensional OCT datasets in biofilm modelling is shown by several examples aiming on an improved understanding of mass transfer at the bulk-biofilm interface. Biotechnol. Bioeng. 2017;114: 1386-1402. © 2017 Wiley Periodicals, Inc.
TL;DR: This spatially incoherent lensless imaging technique is simple and capable of variable focusing with adjustable depths of focus that enables depth sensing of 3D objects that are concealed by the diffusing medium.
Abstract: Scattering media, such as diffused glass and biological tissue, are usually treated as obstacles in imaging. To cope with the random phase introduced by a turbid medium, most existing imaging techniques recourse to either phase compensation by optical means or phase recovery using iterative algorithms, and their applications are often limited to two-dimensional imaging. In contrast, we utilize the scattering medium as an unconventional imaging lens and exploit its lens-like properties for lensless three-dimensional (3D) imaging with diffraction-limited resolution. Our spatially incoherent lensless imaging technique is simple and capable of variable focusing with adjustable depths of focus that enables depth sensing of 3D objects that are concealed by the diffusing medium. Wide-field imaging with diffraction-limited resolution is verified experimentally by a single-shot recording of the 1951 USAF resolution test chart, and 3D imaging and depth sensing are demonstrated by shifting focus over axially separated objects.
TL;DR: A fully automated system using a convolutional neural network for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology and obtained a robust and reliable retina segmentsation even in severe pathological cases is developed.
Abstract: We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.
TL;DR: Polarization sensitive optical coherence tomography (PS-OCT) as mentioned in this paper is an imaging technique based on light scattering that performs rapid two-and three-dimensional imaging of transparent and translucent samples with micrometer scale resolution.
Abstract: Polarization sensitive optical coherence tomography (PS-OCT) is an imaging technique based on light scattering. PS-OCT performs rapid two- and three-dimensional imaging of transparent and translucent samples with micrometer scale resolution. PS-OCT provides image contrast based on the polarization state of backscattered light and has been applied in many biomedical fields as well as in non-medical fields. Thereby, the polarimetric approach enabled imaging with enhanced contrast compared to standard OCT and the quantitative assessment of sample polarization properties. In this article, the basic methodological principles, the state of the art of PS-OCT technologies, and important applications of the technique are reviewed in a concise yet comprehensive way.
TL;DR: This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM).
Abstract: Kawasaki disease (KD) is an acute childhood disease complicated by coronary artery aneurysms, intima thickening, thrombi, stenosis, lamellar calcifications, and disappearance of the media border. Automatic classification of the coronary artery layers (intima, media, and scar features) is important for analyzing optical coherence tomography (OCT) images recorded in pediatric patients. OCT has been known as an intracoronary imaging modality using near-infrared light which has recently been used to image the inner coronary artery tissues of pediatric patients, providing high spatial resolution (ranging from 10 to 20 μm). This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM). The results show the robustness of CNN as the feature extractor and random forest as the classifier with classification rate up to 96%, especially to characterize the second layer of coronary arteries (media), which is a very thin layer and it is challenging to be recognized and specified from other tissues.
TL;DR: A new optical coherence tomography (OCT)-based calcium scoring system to predict stent underexpansion and a calcium volume index (CVI) was developed using 128 pts with pre- and post-stent OCT.
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TL;DR: In this article, a fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in OCT scans.
Abstract: Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.
TL;DR: The OCT angiography allows to detect FAZ enlargement, increased parafoveal capillary nonperfusion, and decreased parafovesal VD in eyes with RVO, and the area of superficial FAZ and the parafovision VD are correlated with best-corrected visual acuity in eyesWith RVO.
Abstract: Purpose:To analyze the correlation of superficial and deep capillary plexuses using optical coherence tomography (OCT) angiography with visual acuity in eyes with retinal vein occlusion (RVO).Methods:We retrospectively reviewed the medical records of 33 patients with retinal vein occlusion (RVO; bra
TL;DR: Small structures in the tissues of living animals are revealed, such as the inner stromal structure of a live mouse cornea, the fine structures inside the mouse pinna, and sweat ducts and Meissner’s corpuscle in the human fingertip skin are revealed.
Abstract: Optical coherence tomography (OCT) is a powerful biomedical imaging technology that relies on the coherent detection of backscattered light to image tissue morphology in vivo. As a consequence, OCT is susceptible to coherent noise (speckle noise), which imposes significant limitations on its diagnostic capabilities. Here we show speckle-modulating OCT (SM-OCT), a method based purely on light manipulation that virtually eliminates speckle noise originating from a sample. SM-OCT accomplishes this by creating and averaging an unlimited number of scans with uncorrelated speckle patterns without compromising spatial resolution. Using SM-OCT, we reveal small structures in the tissues of living animals, such as the inner stromal structure of a live mouse cornea, the fine structures inside the mouse pinna, and sweat ducts and Meissner’s corpuscle in the human fingertip skin—features that are otherwise obscured by speckle noise when using conventional OCT or OCT with current state of the art speckle reduction methods. Optical coherence tomography, a technique that can image inside tissue, is susceptible to speckle noise that limits its diagnostic potential. Here, Libaet al. show that speckle noise can be removed without effectively compromising resolution, revealing previously hidden small structures within tissue.
TL;DR: Choroidal vascularity index may be a potential noninvasive tool for studying structural changes in choroid and monitoring choroidal disease in exudative AMD through spectral domain optical coherence tomography with enhanced depth imaging.
Abstract: Purpose:To evaluate choroidal structural changes in exudative age-related macular degeneration (AMD) using choroidal vascularity index computed from image binarization on spectral domain optical coherence tomography with enhanced depth imaging.Methods:This prospective case series included 42 consecu
TL;DR: This work presents a fully automated 3D method which is able to segment all the retinal layers and fluid-filled regions simultaneously, exploiting their mutual interaction to improve the overall segmentation results.
Abstract: Modern optical coherence tomography (OCT) devices used in ophthalmology acquire steadily increasing amounts of imaging data. Thus, reliable automated quantitative analysis of OCT images is considered to be of utmost importance. Current automated retinal OCT layer segmentation methods work reliably on healthy or mildly diseased retinas, but struggle with the complex interaction of the layers with fluid accumulations in macular edema. In this work, we present a fully automated 3D method which is able to segment all the retinal layers and fluid-filled regions simultaneously, exploiting their mutual interaction to improve the overall segmentation results. The machine learning based method combines unsupervised feature representation and heterogeneous spatial context with a graph-theoretic surface segmentation. The method was extensively evaluated on manual annotations of 20,000 OCT B-scans from 100 scans of patients and on a publicly available data set consisting of 110 annotated B-scans from 10 patients, all with severe macular edema, yielding an overall mean Dice coefficient of 0.76 and 0.78, respectively.
TL;DR: The spiral volumetric optoacoustic tomography technique is introduced that provides spectrally enriched high-resolution contrast across multiple spatiotemporal scales and adds to the multifarious advantages provided by the opto-acoustic technology for structural, functional and molecular imaging.
Abstract: Imaging dynamics at different temporal and spatial scales is essential for understanding the biological complexity of living organisms, disease state and progression. Optoacoustic imaging has been shown to offer exclusive applicability across multiple scales with excellent optical contrast and high resolution in deep-tissue observations. Yet, efficient visualization of multi-scale dynamics remained difficult with state-of-the-art systems due to inefficient trade-offs between image acquisition time and effective field of view. Herein, we introduce the spiral volumetric optoacoustic tomography technique that provides spectrally enriched high-resolution contrast across multiple spatiotemporal scales. In vivo experiments in mice demonstrate a wide range of dynamic imaging capabilities, from three-dimensional high-frame-rate visualization of moving organs and contrast agent kinetics in selected areas to whole-body longitudinal studies with unprecedented image quality. The newly introduced paradigm shift in imaging of multi-scale dynamics adds to the multifarious advantages provided by the optoacoustic technology for structural, functional and molecular imaging. A rapid-fire laser technique from researchers in Germany can image the entire body of a living mouse in sharp, three-dimensional detail. Optoacoustic imaging uses nanosecond-long laser pulses to briefly heat tissue, creating ultrasonic pressure waves that can be used to non-invasively detect tissue shapes. Switching between fields of view in this technique often requires unsafe acquisition times, but Luis Dean-Ben from the Helmholtz Zentrum Munchen research institute and colleagues have solved this issue with spiral volumetric optoacoustic tomography. In this method, the laser beam follows a spiral trajectory around a live mouse and pressure waves are spotted using a spherical detector with 256 sensitive elements. On-the-fly image rendering could capture dynamic events at millisecond time scales, such as beat-by-beat characterization of heart motion to whole-body studies of the growth of breast cancer tumours.
TL;DR: The principle of OCTA involves determining the change in backscattering between consecutive B-scans and then attributing the differences to the flow of erythrocytes through retinal blood vessels and its flow pattern and its potential future applications are summarized.
TL;DR: An overview of the published data on the clinical utility of OCT is provided, highlighting the areas that need further investigation and the current barriers for further adoption of OCT in interventional cardiology practice.
Abstract: The advent of intravascular imaging has been a significant advancement in visualization of coronary arteries, particularly with optical coherence tomography (OCT) that allows for high-resolution imaging of intraluminal and transmural coronary structures. Accumulating data support a clinical role for OCT in a multitude of clinical scenarios, including assessing the natural history of atherosclerosis and modulating effects of therapies, mechanisms of acute coronary syndromes, mechanistic insights into the effects of novel interventional devices, and optimization of percutaneous coronary intervention. In this state-of-the-art review, we provide an overview of the published data on the clinical utility of OCT, highlighting the areas that need further investigation and the current barriers for further adoption of OCT in interventional cardiology practice.
TL;DR: Experimental results in living rabbits demonstrate that the PAM can noninvasively visualize individual depth-resolved retinal and choroidal vessels using a laser exposure dose below the American National Standards Institute safety limit 160 nJ at 570 nm; and the OCT can finely distinguish different retinal layers, the choroid, and the sclera.
Abstract: Most reported photoacoustic ocular imaging work to date uses small animals, such as mice and rats, the eyeball sizes of which are less than one-third of those of humans, posing challenges for clinical translation. Here we developed a novel integrated photoacoustic microscopy (PAM) and optical coherence tomography (OCT) system for dual-modality chorioretinal imaging of larger animals, such as rabbits. The system has quantified lateral resolutions of 4.1 µm (PAM) and 3.8 µm (OCT), and axial resolutions of 37.0 µm (PAM) and 4.0 µm (OCT) at the focal plane of the objective. Experimental results in living rabbits demonstrate that the PAM can noninvasively visualize individual depth-resolved retinal and choroidal vessels using a laser exposure dose of ~80 nJ below the American National Standards Institute (ANSI) safety limit 160 nJ at 570 nm; and the OCT can finely distinguish different retinal layers, the choroid, and the sclera. This reported work may be a major step forward in clinical translation of the technology.