Tomography Modalities Research Articles

Multi-Group Tensor Canonical Correlation Analysis.

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

Diagnosis of Two-Phase Oil/Gas Flow in a Closed Pipe using an 8-Electrode ECT System

Electrical tomography techniques have been developed to monitor the internal behavior of industrial processes. Electrical capacitance gives the best benefits over other tomography modalities, as it has no radiation, is non-intrusive, and has a low cost. This study investigated the diagnosis of two-phase oil/gas flow in a closed pipe, using an image data capture system for an 8 external electrode Electrical Capacitance Tomography (ECT) sensor. The system had a high-resolution ratio, a small measurement error, and was able to remove the effects of stray capacitance. Experimental measurements were carried out on two different materials that filled the space inside the pipe in different proportions to determine the sensitivity and accuracy of the measurement. The results showed that the system had fast image data capture time, high accuracy, a very small resolution ratio, and a good signal-to-noise ratio and quality factor.

Percutaneous Superimposed O-Arm-MRI-Navigated Biopsy for Spinal Column Pathologies.

Classifying spinal tumors can be challenging due to nonspecific clinical and radiological qualities, and a precise biopsy is crucial for an accurate diagnosis and treatment planning. This study aimed to enhance the accuracy and efficiency of spinal biopsies integrating Cone Beam Computed Tomography (CBCT) and magnetic resonance imaging (MRI) modalities using an O-arm CT navigation system. Eighteen patients with different spinal lesions underwent 18 biopsies following the Stealth Station navigation system Spine 8 protocol. Preoperative MRI scans were merged with intraoperative CT navigation systems for continuous monitoring during the biopsy process. The combined imaging technique accurately identified the diseased lesion type in all biopsies, demonstrating 100% sensitivity and specificity. In conclusion, combining MRI and CT imaging modalities significantly improved spinal biopsy accuracy and efficiency, differentiating between pathological entities. However, large-scale studies are desired to validate these findings and investigate potential benefits in different clinical scenarios. Although this method requires general anesthesia, its potential profits in avoiding misdiagnosed lesions and decreasing the need for further invasive procedures make it a promising approach for improving spinal biopsy accuracy and efficiency.

State estimation approach to dual-modal imaging of two-phase flow based on electromagnetic flow tomography and electrical tomography

Monitoring, control and design of industrial processes involving multiphase flows often call for analysis of data from multiple sensors which give information on different quantities of the flowing materials. An example of such case is the problem of monitoring the flow of oil–water mixture: the phase fractions of oil and water, their velocities and volumetric flow rates cannot be retrieved from measurements given by a single sensing/imaging modality. For this reason, multi-modal tomographic imaging systems have been developed. In multi-phase flows, the quantities retrieved from different tomographic instruments are often interconnected—for example, the evolutions of the phase fractions depend on their velocities and vice versa. However, the analysis of data from different tomographic modalities is usually done separately—without taking into account physics that link the quantities of interest. In this paper, we propose a novel approach to image reconstruction in dual-modal tomography of multiphase flows. The governing idea is to combine the two modalities via Bayesian state estimation, that is, we write models that approximate connections between different quantities involved in the process and use sequential measurements from both modalities to jointly estimate these temporally evolving quantities. As an example case, we consider a dual-modal system comprising the electromagnetic flow tomography (EMFT) and electrical tomography (ET). While the EMFT is sensitive to the velocity field but also depends on the phase fractions of fluids, ET measurements are directly linked to phase fractions only. We study the performance of state estimation in EMFT-ET tomography with a set of numerical simulations. The results demonstrate that it outperforms the conventional stationary reconstruction approach, and also provides means for uncertainty quantification in multiphase flow imaging.

Current and potential roles of immuno-PET/-SPECT in CAR T-cell therapy.

Chimeric antigen receptor (CAR) T-cell therapies have evolved as breakthrough treatment options for the management of hematological malignancies and are also being developed as therapeutics for solid tumors. However, despite the impressive patient responses from CD19-directed CAR T-cell therapies, ~ 40%-60% of these patients' cancers eventually relapse, with variable prognosis. Such relapses may occur due to a combination of molecular resistance mechanisms, including antigen loss or mutations, T-cell exhaustion, and progression of the immunosuppressive tumor microenvironment. This class of therapeutics is also associated with certain unique toxicities, such as cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, and other "on-target, off-tumor" toxicities, as well as anaphylactic effects. Furthermore, manufacturing limitations and challenges associated with solid tumor infiltration have delayed extensive applications. The molecular imaging modalities of immunological positron emission tomography and single-photon emission computed tomography (immuno-PET/-SPECT) offer a target-specific and highly sensitive, quantitative, non-invasive platform for longitudinal detection of dynamic variations in target antigen expression in the body. Leveraging these imaging strategies as guidance tools for use with CAR T-cell therapies may enable the timely identification of resistance mechanisms and/or toxic events when they occur, permitting effective therapeutic interventions. In addition, the utilization of these approaches in tracking the CAR T-cell pharmacokinetics during product development and optimization may help to assess their efficacy and accordingly to predict treatment outcomes. In this review, we focus on current challenges and potential opportunities in the application of immuno-PET/-SPECT imaging strategies to address the challenges encountered with CAR T-cell therapies.

Transformer-based cross-modal multi-contrast network for ophthalmic diseases diagnosis

Automatic diagnosis of various ophthalmic diseases from ocular medical images is vital to support clinical decisions. Most current methods employ a single imaging modality, especially 2D fundus images. Considering that the diagnosis of ophthalmic diseases can greatly benefit from multiple imaging modalities, this paper further improves the accuracy of diagnosis by effectively utilizing cross-modal data. In this paper, we propose Transformer-based cross-modal multi-contrast network for efficiently fusing color fundus photograph (CFP) and optical coherence tomography (OCT) modality to diagnose ophthalmic diseases. We design multi-contrast learning strategy to extract discriminate features from cross-modal data for diagnosis. Then channel fusion head captures the semantically shared information across different modalities and the similarity features between patients of the same category. Meanwhile, we use a class-balanced training strategy to cope with the situation that medical datasets are usually class-imbalanced. Our method is evaluated on public benchmark datasets for cross-modal ophthalmic disease diagnosis. The experimental results demonstrate that our method outperforms other approaches. The codes and models are available at https://github.com/ecustyy/tcmn.

Camera spatial arrangement influence on reconstruction accuracy of chemiluminescence tomography.

Computed tomography of chemiluminescence (CTC) has been demonstrated to be a powerful tool for three-dimensional (3D) combustion visualization and measurement, in which the number of cameras and their spatial arrangement significantly impact the tomographic reconstruction quality. In this work, the relationship of the camera spatial arrangement and tomographic reconstruction accuracy is theoretically established based on two-dimensional (2D) and 3D Mojette transforms and their accurate reconstruction conditions. Numerical simulations and experiments were conducted to demonstrate the theories. The results suggest that the exact reconstruction conditions of the Mojette transforms can be used to determine the minimum number of cameras required for tomography reconstruction, and its achieved reliability can be used as an indicator to predict the reconstruction quality. Besides, the 2D coplanar semicircular configuration exhibits a better performance than that of the 3D non-coplanar arrangement. When the 3D non-coplanar arrangement is adopted, the cameras should be widely distributed in the hemispherical space. The related research provides a theoretical basis for the establishment of the CTC system and other tomography modalities.

Radionuclide Tracers for Myocardial Perfusion Imaging and Blood Flow Quantification

Myocardial perfusion imaging by nuclear cardiology is widely validated for the diagnosis, risk stratification, and management of patients with suspected or known coronary artery disease. Numerous radiopharmaceuticals are available for single-photon emission computed tomography and PET modalities. Each tracer shows advantages and limitations that should be taken into account in performing an imaging examination. This review aimed to summarize the state-of-the-art radiotracers used for myocardial perfusion imaging and blood flow quantification, highlighting the new technologic advances and promising possible applications.

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.

Radon transform with Gaussian beam: Theoretical and numerical reconstruction scheme

The Radon transform and its various types have been studied since its introduction by Johann Radon in 1917. Since the Radon transform is an integral transform that maps a given function to its line integral, it has been studied in the field of computerized tomography, which deals with electromagnetic waves that primarily travel along straight lines, such as X-rays. However, in many laser optics applications, it is assumed that the laser beam is shaped like a Gaussian bell rather than a straight line. Therefore, in tomographic modalities using optical beams, such as optical projection tomography, images reconstructed with the inversion algorithms for the standard Radon transform are subject to a loss of quality. To address this issue, one needs to consider theoretical inversion methods for Radon transforms with Gaussian beam kernels and associated numerical reconstruction methods. In this study, we consider a Radon transform with a Gaussian beam kernel (also known as the point spread function) and show the uniqueness of the inversion of this transform. Furthermore, we provide an accurate and stable numerical reconstruction algorithm using the point spread function-sequential quadratic Hamiltonian scheme. Numerical experiments with disk and Shepp–Logan phantoms demonstrate that the proposed framework provides superior reconstructions compared to the traditional filtered back-projection algorithm.

Lung nodule detection in chest CT images based on vision transformer network with Bayesian optimization

PurposeLung cancer patients are usually diagnosed by the Computed tomography (CT) modality with the help of radiologists. Frequently, radiologists manually analyse CT images, which is a big task that consumes more time and results in inaccuracy. Therefore, this study proposes an automated computer-aided diagnosis (CAD) scheme based on Vision Transformer architecture with Bayesian Optimisation for lung nodule detection to assist radiologists in decision-making. MethodsThe Vision Transformer architecture relies on the shifted window-based vision transformer encoders to improve the processing and propagation efficiency of the lung nodule features. The baseline detection model is developed by integrating shifted window-based vision transformer for deep nodule feature extractions and a region proposal network to predict candidate nodules with low network complexity. The Bayesian Optimisation with the Gaussian method was used to optimise the hyperparameter values from the baseline model suitable for lung nodule detection on chest CT images. ResultsWe used 888 CT images with 10-fold cross-validation from the Lung Nodule Analysis 2016 (LUNA16) public dataset to validate the proposed network. Free-response receiver operating characteristics and competition performance metrics (CPM) was used for the performance assessment. The proposed architecture obtained the highest detection sensitivity of 98.39% and the CPM score of 0.909 with few network parameters. ConclusionThis study proposes a CAD system based on a vision transformer network determined by Bayesian Optimisation for lung nodule detection in chest CT images. The empirical results prove the proposed algorithm's effectiveness compared to the state-of-the-art methods.

Morphometric Analysis and Linear Measurements of the Scala Tympani and Implications in Cochlear Implant Electrodes.

The objective of this study was to perform detailed height and cross-sectional area measurements of the scala tympani in histologic sections of nondiseased human temporal bones and correlate them with cochlear implant electrode dimensions. Previous investigations in scala tympani dimensions have used microcomputed tomography or casting modalities, which cannot be correlated directly with microanatomy visible on histologic specimens. Three-dimensional reconstructions of 10 archival human temporal bone specimens with no history of middle or inner ear disease were generated using hematoxylin and eosin histopathologic slides. At 90-degree intervals, the heights of the scala tympani at lateral wall, midscala, and perimodiolar locations were measured, along with cross-sectional area. The vertical height of the scala tympani at its lateral wall significantly decreased from 1.28 to 0.88 mm from 0 to 180 degrees, and the perimodiolar height decreased from 1.20 to 0.85 mm. The cross-sectional area decreased from 2.29 (standard deviation, 0.60) mm 2 to 1.38 (standard deviation, 0.13) mm 2 from 0 to 180 degrees ( p = 0.001). After 360 degrees, the scala tympani shape transitioned from an ovoid to triangular shape, corresponding with a significantly decreased lateral height relative to perimodiolar height. Wide variability was observed among the cochlear implant electrode sizes relative to scala tympani measurements. The present study is the first to conduct detailed measurements of heights and cross-sectional area of the scala tympani and the first to statistically characterize the change in its shape after the basal turn. These measurements have important implications in understanding locations of intracochlear trauma during insertion and electrode design.

Nanoparticles labeled with gamma-emitting radioisotopes: an attractive approach for in vivo tracking using SPECT imaging.

Providing accurate molecular imaging of the body and biological process is critical for diagnosing disease and personalizing treatment with the minimum side effects. Recently, diagnostic radiopharmaceuticals have gained more attention in precise molecular imaging due to their high sensitivity and appropriate tissue penetration depth. The fate of these radiopharmaceuticals throughout the body can be traced using nuclear imaging systems, including single-photon emission computed tomography (SPECT) and positron emission tomography (PET) modalities. In this regard, nanoparticles are attractive platforms for delivering radionuclides into targets because they can directly interfere with the cell membranes and subcellular organelles. Moreover, applying radiolabeled nanomaterials can decrease their toxicity concerns because radiopharmaceuticals are usually administrated at low doses. Therefore, incorporating gamma-emitting radionuclides into nanomaterials can provide imaging probes with valuable additional properties compared to the other carriers. Herein, we aim to review (1) the gamma-emitting radionuclides used for labeling different nanomaterials, (2) the approaches and conditions adopted for their radiolabeling, and (3) their application. This study can help researchers to compare different radiolabeling methods in terms of stability and efficiency and choose the best way for each nanosystem.

Evaluating Hydroxyapatite, Gold Nanoparticles, and Graphene-Copper as Bimodal Agents for X-ray and Computed Tomography.

A global need exists for new and more effective contrast agents for computed tomography and traditional X-ray modalities. Among the few options available nowadays, limitations imposed by industrial production, performance, and efficacy restrict the use and reduce the potential of both imaging techniques. The use of nanomaterials as new contrast agents for X-ray and computed tomography is an innovative and viable way to increase the options and enhance performance. In this study, we evaluated eight nanomaterials: hydroxyapatite doped with zinc (Zn-HA 10%); hydroxyapatite doped with strontium (Sr-HA 10%); hydroxyapatite without thermal treatment (HA 282 STT); thermally treated hydroxyapatite (HA 212 500 °C and HA 01.256 CTT 1000 °C); hydroxyapatite microspheres (HA microspheres); gold nanoparticles (AuNP); and graphene oxide doped with copper (Cu-GO). The results showed that for both imaging modalities; HA microspheres were the best option, followed by hydroxyapatite thermally treated at 1000 °C. The nanomaterials with the worst results were hydroxyapatite doped with zinc (Zn-HA 10%), and hydroxyapatite doped with strontium (Sr-HA 10%). Our data demonstrated the potential of using nanomaterials, especially HA microspheres, and hydroxyapatite with thermal treatment (HA 01.256 CTT 1000 °C) as contrast agents for X-ray and computed tomography.

Intraoperative Assessment of Surgical Margins in Cancer Resection Surgery Via the Sub-Diffuse Optical Tomography "S-DOT"

In Head and Neck Squamous Cell Carcinomas (HNSCC), positive margins after surgical resection dominate the clinical outcome of patients. Between 15-30 precent of all HNSCC surgeries result in a positive margin (&gt;5mm) that requires postoperative chemo-radiation, radiotherapy, and/or revision surgeries. To minimize local recurrence, margin assessment is typically performed through surgical pathology departments; however, this technique often takes more than 24 hours to process and only a small fraction of the total surgical margin is evaluated. A second, intraoperativeapproach used by surgeons to minimize local recurrence is frozen section analysis (FSA), which in time intensive and suffers heavily from sampling errors. To effect change, we have hypothesized a sub-diffuse optical tomography modality that can identify the differences in depth sensitivity of the fluorescence, Cetuximab-IRDye800CW conjugate (ICON), by the fluorescent photons collected from sub-diffused media as a function of the photon exit angle from the excited tissue. Based on promising preliminary simulations using Monte Carlo MATLAB, closed vs. open aperture fluorescence imaging in biological tissue demonstrated an enhanced true depth of fluorescence resolution up to 6mm, well beyond the insufficient 5mm mark. By identifying the deep margins from the fluorescent peaks within 5 minutes, we hope to improve complete oncological surgery by providing surgeons an exact region of interest on the specimen where the tumor is closest to the edge, so they may continue surgery by providing sugeons an exact region of interest on the specimen where the tumor is closest to the edge, so they may continue surgery on the spot to minimize the probability of cancer being left behind.

Imaging of Central Nervous System Autoimmune, Paraneoplastic, and Neuro-rheumatologic Disorders.

This article provides an overview of the imaging modalities used in the evaluation of central nervous system (CNS) autoimmune, paraneoplastic, and neuro-rheumatologic disorders. An approach is outlined for interpreting imaging findings in this context, synthesizing a differential diagnosis based on certain imaging patterns, and choosing further imaging for specific diseases. The rapid discovery of new neuronal and glial autoantibodies has revolutionized the autoimmune neurology field and has elucidated imaging patterns characteristic of certain antibody-associated diseases. Many CNS inflammatory diseases, however, lack a definitive biomarker. Clinicians should recognize neuroimaging patterns suggestive of inflammatory disorders, as well as the limitations of imaging. CT, MRI, and positron emission tomography (PET) modalities all play a role in diagnosing autoimmune, paraneoplastic, and neuro-rheumatologic disorders. Additional imaging modalities such as conventional angiography and ultrasonography can be helpful for further evaluation in select situations. Knowledge of imaging modalities, both structural and functional, is critical in identifying CNS inflammatory diseases quickly and can help avoid invasive testing such as brain biopsy in certain clinical scenarios. Recognizing imaging patterns suggestive of CNS inflammatory diseases can also facilitate the early initiation of appropriate treatments to diminish morbidity and future disability.

Imaging Recommendations for Diagnosis, Staging, and Management of Adrenal Tumors

AbstractAdrenal glands are affected by a wide variety of tumors apart from infective and inflammatory lesions and their noninvasive characterization on imaging is important for the management of these patients. Incidentalomas form the major bulk of adrenal tumors and differentiation of benign adenomas from other malignant lesions, especially in patients with a known malignancy, guide further management. Imaging is an integral part of management along with clinical and biochemical features. The cornerstone of clinical and biochemical evaluation of adrenal tumors is to determine whether the lesion is functional or nonfunctional. Computed tomography (CT) is considered as the workhorse for imaging evaluation of adrenal lesions. CT densitometry and CT contrast washout characteristics are quite reliable in differentiating adenomas from malignant lesions. CT is also the modality of choice for the evaluation of resectability and staging of primary adrenal tumors. Magnetic resonance imaging (MRI) has superior contrast resolution compared to other morphological imaging modalities and is generally used as a problem-solving tool. MRI chemical shift imaging can also be used to reliably detect adrenal adenomas. Ultrasonography (USG) is used as a screening tool that is usually followed by either CT or MRI to better characterize the tumor and it is not routinely used for assessing the resectability, staging, and characterization of adrenal tumors. Another important role of USG is in image-guided sampling of tumors. Fluorodeoxyglucose positron emission tomography-computed tomography and other nuclear medicine modalities are a valuable addition to morphological imaging modalities. Image-guided interventions also play an important role in obtaining tissue samples where diagnostic imaging is not able to characterize adrenal tumors. In the functioning of adrenal tumors, adrenal venous sampling is widely used to accurately lateralize the secreting tumor.

Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.

Reducing the radiation exposure experienced by patients in total-body computed tomography (CT) imaging has attracted extensive attention in the medical imaging community. A low radiation dose may result in increased noise and artifacts that greatly affect the subsequent clinical diagnosis. To obtain high-quality total-body low-dose CT (LDCT) images, previous deep learning-based research works developed various network architectures. However, most of these methods only employ normal-dose CT (NDCT) images as ground truths to guide the training process of the constructed denoising network. As a result of this simple restriction, the reconstructed images tend to lose favorable image details and easily generate oversmoothed textures. This study explores how to better utilize the information contained in the feature spaces of NDCT images to guide the LDCT image reconstruction process and achieve high-qualityresults. We propose a novel intratask knowledge transfer (KT) method that leverages the knowledge distilled from NDCT images as an auxiliary component of the LDCT image reconstruction process. Our proposed architecture is named the teacher-student consistency network (TSC-Net), which consists of teacher and student networks with identical architectures. By employing the designed KT loss, the student network is encouraged to emulate the teacher network in the representation space and gain robust prior content. In addition, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced. The CRM aims to minimize and maximize the L2 distances from the predicted CT images to the NDCT samples and to the LDCT samples in the latent space, respectively. Moreover, based on attention and the deformable convolution approach, we design a dynamic enhancement module (DEM) to improve the network capability to transform input informationflows. By conducting ablation studies, we prove the effectiveness of the proposed KT loss, CRM, and DEM. Extensive experimental results demonstrate that the TSC-Net outperforms the state-of-the-art methods in both quantitative and qualitative evaluations. Additionally, the excellent results obtained for clinical readings also prove that our proposed method can reconstruct high-quality CT images for clinicalapplications. Based on the experimental results and clinical readings, the TSC-Net has better performance than other approaches. In our future work, we may explore the reconstruction of LDCT images by fusing the positron emission tomography (PET) and CT modalities to further improve the visual quality of the reconstructed CTimages.

Ophthalmic Manifestations, Evaluation, and Guidelines for Testing of Concussion

Early detection and treatment of concussions is crucial to preventing further brain damage. Current subjective standard-of-care tests used to diagnose concussions, such as symptom reporting and standardized concussion checklists, can often underdiagnose patients with concussions. This review will cover novel biomarkers of concussions related to concussion-associated visual deficits and how they can be used to more accurately monitor patient concussion symptom improvement. Visual deficits are seen in up to 90% of patients within hours after a concussion-inducing trauma and can serve as objective biomarkers in diagnosing and monitoring concussions. Some of the key visual deficits that are seen in patients with concussions include convergence insufficiency, problems with accommodation and smooth pursuit eye movements, saccade dysfunction, and decreased optokinetic nystagmus gain. Patients frequently report blurred vision, challenges reading, double vision and eyestrain, dizziness, visual field defects, and light sensitivity, related to concussions. Promising eye tests to detect and track concussions include pupillary light reflexes, the circle test, and the Samandarani group’s non-spatially calibrated binocular motility test/algorithm in conjunction with video oculography and eye tracking equipment. The EYE-SYNC eye-tracking device allows for portable and accurate detection of eye movements in the field and is a promising tool for detecting concussions both in the field and clinic. Optical coherence tomography and other retinal imaging modalities also represent a promising method of identifying individuals who have sustained a concussion.

A complete clinical review of idiopathic macular telangiectasia.

Idiopathic macular telangiectasia refers to a disease entity characterized by telangiectasia and alteration of the juxtafoveal capillary network. It can present uniocularly or binocularly and is subdivided into three groups which have varied appearances. Group I occurs in males which is easily identifiable and has macular edema. Group II has no sex predilection and difficult to distinguish by clinical examination and often requires the need of optical coherence tomography and angiographic modalities. Group III is not so common with its progressive nature and associated with neurological diseases. This article is aimed to provide complete details about idiopathic macular telangiectasia/idiopathic juxtafoveal telangiectasia/parafoveal telangiectasia including various groups, pathology, presentation, and management.