Optical Coherence Tomography Modalities Research Articles

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

RMAP-ResNet: Segmentation of brain tumor OCT images using residual multicore attention pooling networks for intelligent minimally invasive theranostics

Background and objectiveThe occurrence of brain tumors seriously affects patients both mentally and physically, reducing their quality of life. Automatic recognition and segmentation of tumor boundaries are essential for computer-aided diagnosis and treatment. While optical coherence tomography (OCT) has been widely employed in the detection and analysis fundus lesions and vascular segmentation due to its high resolution and real-time imaging advantages, the segmentation of brain tumor OCT images based on deep neural network has not been studied. However, despite the encoding and decoding structure based on deep learning has achieved good results in medical image segmentation, these models still face limitations in accurately segmenting tumor OCT images due to variations in tumor shapes, sizes, and numbers, and the low contrast of OCT images. Accurate segmentation of tumor tissue contours is crucial as the absence of this mechanism hampers the effective application of OCT imaging methods for brain tumor diagnosis and treatment in clinical practice. Therefore, we propose a novel neural network that can accurately segment brain tumors and their residual tissues in OCT images. MethodsIn this study, we propose a novel residual multi-core attention pooling network (RMAP ResNet) for the segmentation of tumorous and its residual tissue. The RMAP module primarily relies on multiple associative effective receptive fields. These fields enable the detection of targets of various sizes. The module also incorporates an attention structure to adjust connection weights, suppressing irrelevant information and emphasizing the characteristics of tumor OCT images. This process restores the contextual dependencies. ResultsWe validated our proposed RMAP ResNet method on the dataset of mouse tumor OCT imaging obtained using the sweep source OCT imaging system. The experimental results demonstrate that RMAP ResNet achieves superior performance in tumor tissue segmentation. The proposed method improves the quality of tumor segmentation for various shapes and sizes, leading to superior results. RMAP ResNet achieved Dice and IoU values of 94.78% and 90.58%, respectively, confirming the effectiveness and advancement of our proposed technology. ConclusionsOur proposed method addresses the crucial issue of the limited use of the OCT imaging modality in tumor resection surgery. It overcomes the challenge of visually identifying the tumor boundary in OCT modality imaging, where physicians using the naked eye may encounter difficulties. This study has potential applications in the fields of surgical navigation and intelligent minimally invasive treatment of brain tumors.

Bond-selective full-field optical coherence tomography.

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Conventional OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bond-selective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 µm PMMA beads embedded in agarose gel. Next, we show 3D hyperspectral imaging of up to 75 µm of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FF-OCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on imaging a highly scattering myelinated axons region in a mouse brain tissue slice.

Deep learning for artery-vein classification in optical coherence tomography angiography.

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging. However, it cannot discern capillary-level structures due to the limited image contrast. As a functional extension of OCT modality, optical coherence tomography angiography (OCTA) is a non-invasive, label-free method for enhanced contrast visualization of retinal vasculatures at the capillary level. Recently differential artery-vein (AV) analysis in OCTA has been demonstrated to improve the sensitivity for staging of retinopathies. Therefore, AV classification is an essential step for disease detection and diagnosis. However, current methods for AV classification in OCTA have employed multiple imagers, that is, fundus photography and OCT, and complex algorithms, thereby making it difficult for clinical deployment. On the contrary, deep learning (DL) algorithms may be able to reduce computational complexity and automate AV classification. In this article, we summarize traditional AV classification methods, recent DL methods for AV classification in OCTA, and discuss methods for interpretability in DL models.

Choroidal vessel segmentation in SD-OCT with 3D Shape-aware Adversarial Networks

Background and Objective:Quantitative analysis of choroidal vessels is important for the diagnosis of choroidal related diseases and the cognition of the choroid. Previous studies focused on segmenting choroidal vessels in enhanced depth imaging optical coherence tomography (OCT) and swept-source OCT modalities. There is still a blank for the segmentation of 3D choroidal vessels in the spectral-domain OCT (SD-OCT) modality. Quantitative analysis of choroidal vessels is crucial for diagnosing choroidal-related diseases and understanding the choroid. Previous studies have focused on segmenting choroidal vessels in enhanced depth imaging optical coherence tomography (OCT) and swept-source OCT modalities. There is still a gap in the segmentation of 3D choroidal vessels in the spectral-domain OCT (SD-OCT) modality. Method:We propose a novel 3D CNN-based method, called Shape-aware Adversarial Networks (SAN), for segmenting choroidal vessels in SD-OCT images. To learn the 3D mapping of SD-OCT images and labels, we built a 3D U-shape network backbone with anisotropic down-sampling and up-sampling operations. To learn the morphological characteristics of choroidal vessels, we constructed an auxiliary output branch to predict a newly designed choroidal vessel shape representation generated by labels. We further introduced a label-conditional adversarial loss to enforce shape learning in a patch-based pipeline. The code for SAN is publicly available at: https://github.com/nicetomeetu21/SAN. Results:The effectiveness of SAN has been verified in data with myopia. The proposed model reported as measures of segmentation performance a mean dice similarity coefficient of 85.96% and a mean Hausdorff distance of 2.54, outperforming other 2D and 3D segmentation methods. We further applied our method in the statistical analysis of the choroidal feature between 100 eyes with low to moderate myopia and 108 eyes with high myopia. The results show that in all Early Treatment of Diabetic Retinopathy Study subfields, the choroid volumes and vascular volumes of the two groups are significantly different (P<0.01). The choroidal vascular indexes (CVI) of two groups are significantly different in most subfields (P<0.01) except for central and nasal inner subfields. The CVI of the high myopia group is higher than that of the low to moderate myopia group in most subfields except for the nasal inner subfield. Conclusions:The results demonstrate that the proposed method can accurately segment choroidal vessels from SD-OCT volumes. The quantitative analysis of choroidal biomarkers in a total of 208 myopia eyes shows the applicability of the proposed method in helping to study the choroid.

Quantification of elastic anisotropy of human skin in vivo with dynamic optical coherence elastography and polarization-sensitive OCT

The complex structure of skin ensures a broad range of biological functions. The loss of a large skin area due to trauma or disease usually requires reconstructive surgery that often involves grafting or flapping. The clinical need for non-contact objective measurements of skin elasticity is necessary to guide grafting procedures to minimize complications such as secondary contracture or hypertrophic scars. Here, we report a non-contact and non-invasive method combining acoustic micro-tapping optical coherence elastography (AμT-OCE) with polarization-sensitive (PS-) optical coherence tomography (OCT) to quantify anisotropic elastic properties of skin. We show that all three elastic constants that define skin’s anisotropic elastic deformation and the orientation of collagen fibers in the dermis can be determined from propagating elastic waves over the skin surface. Measurements were performed on healthy volunteers in vivo. A nearly incompressible transverse isotropic model of skin elasticity was used to reconstruct the moduli from experimental data after being validated in extensive numerical simulations. Finally, we demonstrate that combining several OCT modalities (structural OCT, OCT angiography, PS-OCT, and AμT-OCE) may provide rich information about skin and demonstrate the potential for complex characterization of scar.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images.

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models’ performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists’ (experts) precious time and involvement.

One-year outcome of neovascular age-related macular degeneration patients followed-up using different optical coherence tomography modalities.

The aim of this study is to compare the one year outcome of neovascular age-related macular degeneration (nAMD) patients treated by a PRN regimen of Anti-vascular endothelial growth factor (VEGF) intravitreal injections (IVTs), using optical coherence tomography B-scan (OCT-B) or OCT Angiography (OCT-A) imaging modalities during follow-up. Patients older than 50 years with nAMD currently treated by PRN regimen of Anti-VEGF IVTs were recruited from Rothschild Foundation Hospital - Paris and Centre Ophtalmologique Maison Rouge - Strasbourg and followed-up for a year. Patients were randomized in two groups: one group was followed by OCT-B while the other was followed by OCT-A. Thirty-three patients were followed by OCT-A and 31 patients were followed by OCT-B. Twenty-nine patients in the OCT-A group and 27 patients in the OCT-B group attended the last visit. No statistically significant difference was found between the two groups at 1 year concerning: improvement or stabilization of the best corrected distance visual acuity (BCVA) (p > 0.9), exudative signs (p > 0.9), number of injections (p = 0.8) and the delay until the first reinjection was performed (p = 0.5). The use of OCT-A or OCT-B as imaging modalities in nAMD treated by a PRN regimen of Anti-VEGF IVTs seem to be comparable at one year.

Visualization of Anterior Chamber Angle Structures With Scattering- and Polarization-Sensitive Anterior Segment Optical Coherence Tomography.

PurposeThe purpose of this study was to compare three optical coherence tomography (OCT) modalities in the observation of anterior chamber angle structures; trabecular meshwork (TM), Schlemm's canal (SC), and band of extracanalicular limbal lamina (BELL).MethodsThree OCT modalities were used: (1) 2 × 2 Jones-matrix scattering OCT (S-OCT) representing conventional intensity OCT, (2) polarization-diverse S-OCT that was calculated as summation of all elements of the Jones-matrix to eliminate the influence of artifacts caused by sample birefringence, and (3) polarization-sensitive OCT (PS-OCT) to assess depth-resolved phase retardation.ResultsIn a total of 97 eyes of 55 subjects, nasal and temporal angles were scanned. The detection rate of TM and BELL was significantly different among modalities; highest with PS-OCT (95.1% and 99.2%), followed by 2 × 2 Jones-matrix S-OCT (71.1% and 88.7%) and polarization-diverse S-OCT (33.2% and 25.0%), indicating the influence of artifacts on 2 × 2 Jones-matrix S-OCT measurements. SC was visible with 2 × 2 Jones-matrix S-OCT, polarization-diverse S-OCT, and PS-OCT in 14.2%, 14.9%, and 0.3% of images, respectively. The intergrader agreement as evaluated with the prevalence-adjusted bias-adjusted κ value was higher with PS-OCT than with other S-OCTs.ConclusionsVisibility of anterior chamber angle structures was assessed with three OCT modalities. For TM and BELL that are rich in collagen fibers, PS-OCT provides significantly better visibility than S-OCT without the influence of artifacts arising from polarization or birefringence. Visualization of SC was more difficult with any OCT modalities.Translational RelevancePS-OCT is a useful tool to investigate the anterior chamber angle structures which are difficult to observe with conventional OCT.

Optical coherence tomography

During the past decade, noninvasive imaging has emerged as a valuable tool in clinical dermatology and dermatologic research. Optical coherence tomography (OCT) is one such type of noninvasive imaging. OCT uses the principle of interferometry to produce real-time images. A low-power diode laser shines infrared light onto tissues, which reflects back to an optical fiber interferometer. Using time delay and the backscattered light intensity, a two-dimensional image akin to an ultrasound is rendered. We review the history, types, and modalities of OCT, plus the many applications of frequency domain, high definition, and dynamic OCT in practice, including its utility in diagnosis, monitoring, and grading disease severity in a variety of cutaneous conditions.

Data augmentation for patch-based OCT chorio-retinal segmentation using generative adversarial networks

Many clinical and research tasks rely critically upon the segmentation of tissue layers in optical coherence tomography (OCT) images of the posterior eye (the retina and choroid). However, a major limitation of using machine learning-based segmentation methods is that performance depends on a large quantity of and diversity in the data used to train the models. Due to their demonstrated ability to generate high quality and diverse synthetic images, we propose the application of GANs here to augment data for a patch-based approach to OCT chorio-retinal boundary segmentation. Given the complexity of GAN training, a range of experiments are performed to understand and optimise performance. We show that it is feasible to generate patches that are visually indistinguishable from their real variants and in the best case, the segmentation performance utilising solely synthetic data is nearly comparable to a model trained on real data. The data augmentation capabilities are demonstrated with classification performance improvements realised on a range of sparse datasets. These findings highlight the potential use of GANs for data augmentation in future work with chorio-retinal OCT images. Additionally, this study includes a range of experimental findings and an analysis of techniques which may be useful for developing or improving GAN-based methods that are not necessarily limited to chorio-retinal images, the OCT modality, or data augmentation.

Code-free deep learning for multi-modality medical image classification

A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Applications and future directions for optical coherence tomography in dermatology*

Optical coherence tomography (OCT) is a noninvasive optical imaging method that can generate high-resolution en face and cross-sectional images of the skin in vivo to a maximum depth of 2mm. While OCT holds considerable potential for noninvasive diagnosis and disease monitoring, it is poorly understood by many dermatologists. Here we aim to equip the practising dermatologist with an understanding of the principles of skin OCT and the potential clinical indications. We begin with an introduction to the technology and discuss the different modalities of OCT including angiographic (dynamic) OCT, which can image cutaneous blood vessels at high resolution. Next we review clinical applications. OCT has been most extensively investigated in the diagnosis of keratinocyte carcinomas, particularly basal cell carcinoma. To date, OCT has not proven sufficiently accurate for the robust diagnosis of malignant melanoma; however, the evaluation of abnormal vasculature with angiographic OCT is an area of active investigation. OCT, and in particular angiographic OCT, also shows promise in monitoring the response to therapy of inflammatory dermatoses, such as psoriasis and connective tissues disease. We additionally discuss a potential role for artificial intelligence in improving the accuracy of interpretation of OCT imaging data.

Ex-vivo Alzheimer's disease brain tissue investigation: a multiscale approach using 1060-nm swept source optical coherence tomography for a direct correlation to histology.

.Significance: Amyloid-beta () plaques are pathological protein deposits formed in the brain of Alzheimer’s disease (AD) patients upon disease progression. Further research is needed to elucidate the complex underlying mechanisms involved in their formation using label-free, tissue preserving, and volumetric techniques.Aim: The aim is to achieve a one-to-one correlation of optical coherence tomography (OCT) data to histological micrographs of brain tissue using 1060-nm swept source OCT.Approach: plaques were investigated in ex-vivo AD brain tissue using OCT with the capability of switching between two magnifications. For the exact correlation to histology, a 3D-printed tool was designed to generate samples with parallel flat surfaces. Large field-of-view (FoV) and sequentially high-resolution volumes at different locations were acquired. The large FoV served to align the OCT to histology images; the high-resolution images were used to visualize fine details.Results: The instrument and the presented method enabled an accurate correlation of histological micrographs with OCT data. plaques were identified as hyperscattering features in both FoV OCT modalities. The plaques identified in volumetric OCT data were in good agreement with immunohistochemically derived micrographs.Conclusion: OCT combined with the 3D-printed tool is a promising approach for label-free, nondestructive, volumetric, and fast tissue analysis.

High-resolution multimodal photoacoustic microscopy and optical coherence tomography image-guided laser induced branch retinal vein occlusion in living rabbits

Joint high-resolution multimodal photoacoustic microscopy (PAM) and optical coherence tomography (OCT) was developed to improve the efficiency for visualizing newly developed retinal neovascularization (RNV) and to monitor the dynamic changes of retinal vein occlusion (RVO) in living rabbits. The RNV and RVO models were created in New Zealand rabbits by Rose Bengal laser-induced RVO. Dual modalities imaging equipment, including color fundus photography, fluorescein angiography (FA), OCT, and PAM, was used to image and assess the changes of retinal vasculature. In vivo experimental results exhibited that not only the treatment boundaries and the position of the occluded vasculature but also the structure of individual RNV were markedly observed using PAM platform with great resolution and high image contrast. The laser light energy of 80 nJ was used to induce photoacoustic signal, which is approximately half the energy of the American National Standards Institute safety limit. A cross-sectional structure of RNV was identified with the OCT modality. Furthermore, vibrant transformations in the RNV and the retinal morphology were examined at different times after laser occlusion: days 4, 28, 35, 49, and 90. PAM revealed high contrast and high resolution vascular imaging of the retina and choroid with amplified penetration depth. Through the present custom-built imaging system, both RNV and RVO can be reconstructed and observed in two and three dimensions. A unique dual modality A unique dual modality PAM and OCT can help precisely visualize and distinguish individual microvessels, microvessel depth, and the surrounding anatomy. Thus, the proposed multimodal ocular imaging platform may offer a potential equipment to enhance classification of microvasculature in a reliable and proficient manner in larger rabbit eyes.

A bimodal endoscopic imager in a glass package

We present the first endoscopic imaging probe that combines full-field white light video microscopy and 3D optical coherence tomography (OCT) in a single miniaturized instrument. Designed as an instrument for the working channels of conventional endoscopes, the presented system has dimensions of mm3 (l × w × h) and is an important step towards practical multi-modal optical biopsy systems for clinical use. The video modality has a sensor-limited resolution and a total field of view of 10 μm and 1.17 mm in diameter, respectively. The OCT modality works in a scanned imaging arrangement and provides 3D optical tomograms with a lateral resolution of 43.5 μm. An integrated tubular piezoelectric fiber scanner is used to perform en face scanning necessary for the 3D OCT measurements. The entire system is assembled and encapsulated within a glass package fabricated via 3D laser microstructuring, a technology which addresses the major issues of micro-optic assembly precision and biocompatibility.

Adaptable switching schemes for time-encoded multichannel optical coherence tomography.

We introduce the approach of variable time encoding for multichannel optical coherence tomography (OCT). High-speed fiber optical switches are applied for sequential sample arm switching to enable quasisimultaneous image acquisition from three different orientation angles. In comparison with previous multichannel OCT (using simultaneous sample illumination), time-encoded multichannel OCT has no need for division of illumination power among the respective channels to satisfy laser safety requirements. Especially for ophthalmic applications-in particular retinal imaging, which the presented prototype was developed for-this advantage strongly influences image quality through an enhanced sensitivity. Nevertheless, time encoding comes at the cost of a decrease in imaging speed due to sequential channel illumination. For the typical multichannel OCT modality Doppler OCT, this results in a reduction of the maximum unambiguously determinable Doppler velocity. However, we demonstrate that this drawback can be overcome by adaptation of the illumination channel switching scheme. Thus, a re-extension of the maximum unambiguously determinable Doppler frequency to the full A-scan rate of the tunable light source is presented. The performance of the technique is demonstrated by flow phantom experiments and measurements of retinal blood flow in the eyes of healthy human volunteers.

Wavelet and shearlet-based image representations for visual servoing

A visual servoing scheme consists of a closed-loop control approach that uses visual information feedback to control the motion of a robotic system. Probably the most popular visual servoing method is image-based visual servoing (IBVS). This kind of method uses geometric visual features extracted from the image to design the control law. However, extracting, matching, and tracking geometric visual features over time significantly limits the versatility of visual servoing controllers in various industrial and medical applications, in particular for “low-structured” medical images, e.g. ultrasounds and optical coherence tomography modalities. To overcome the limits of conventional IBVS, one can consider novel visual servoing paradigms known as “direct” or “featureless” approaches. This paper deals with the development of a new generation of direct visual servoing methods in which the signal control inputs are the coefficients of a multiscale image representation. In particular, we consider the use of multiscale image representations that are based on discrete wavelet and shearlet transforms. Up to now, one of the main obstacles in the investigation of multiscale image representations for visual servoing schemes was the issue of obtaining an analytical formulation of the interaction matrix that links the variation of wavelet and shearlet coefficients to the spatial velocity of the camera and the robot. In this paper, we derive four direct visual servoing controllers: two that are based on subsampled respectively non-subsampled wavelet coefficients and two that are based on the coefficients of subsampled respectively non-subsampled discrete shearlet transforms. All proposed controllers were tested in both simulation and experimental scenarios (using a six-degree-of-freedom Cartesian robot in an eye-in-hand configuration). The objective of this paper is to provide an analysis of the respective strengths and weaknesses of wavelet- and shearlet-based visual servoing controllers.

Optic disc drusen: understanding an old problem from a new perspective.

Optic disc drusen (ODD) are acellular deposits located in the optic nerve head of up to 2.4% of the population. They may develop as by-products of impaired axonal metabolism in genetically predisposed individuals, in whom a narrow scleral canal is hypothesized to play a role. Although ODD are often considered as benign innocent bystanders, recognized as part of a routine ophthalmological examination, the vast majority of patients with ODD have visual field defects. Optic disc drusen (ODD)-associated complications with severe visual loss, most often due to anterior ischaemic optic neuropathy, are also known to occur. There are no treatments available to prevent or ameliorate the vision loss caused by ODD. In children, the ODD are usually uncalcified and buried within the optic nerve head tissue. In these cases, the condition can be difficult to diagnose, as it often resembles a papilloedema with optic nerve head swelling caused by raised intracranial pressure. During the teenage years, the ODD progressively become more calcified and probably also larger, which allow them to be visible on ophthalmoscopy. With the advent and proper utilization of high-resolution modalities of optical coherence tomography (OCT), it has now become possible to detect even the smallest and most deeply located ODD. This allows for ODD detection at a much earlier developmental stage than has previously been possible and enhances the possibilities of research in underlying mechanisms. A review of the literature on ODD was conducted using the PUBMED database. The review focuses on the current knowledge regarding pathogenesis, diagnostics, clinical disease-tracking methodologies, structure-function relationships and treatment strategies of ODD.

Neuroimaging diagnostic and monitoring approaches in ophthalmology.

We review new applications of optical coherence tomography (OCT) technology in neuro-ophthalmology. We also describe new technologies for visualizing the extracranial vessels in the diagnosis of giant cell arteritis (GCA). Newer OCT modalities are expanding the evaluation of the optic disc, and are being applied to a number of neurologic conditions such as demyelinating and neurodegenerative disease. Swept-source OCT and enhanced-depth imaging OCT are refining the fine-grained analysis of the optic nerve head in the diagnosis of papilledema and optic nerve drusen. OCT-angiography is opening up new avenues to the study of the vasculature of the optic nerve head and its disorders, including ischemic optic neuropathy. Newer technologies in the diagnosis of GCA include vascular ultrasound, magnetic resonance imaging (MRI) of the extracranial vasculature and PET imaging of the large vessels. OCT and several of its derivations are advancing diagnosis, and in some cases prognostication, in a variety of inflammatory, ischemic and compressive optic neuropathies. These technologies hold potential in the laboratory as well, yielding insights into the mechanisms of a variety of neurological conditions. In addition, further developments in MRI and ultrasonography techniques are shaping the approach to the diagnosis of GCA.