Prediction of motion artifacts caused by translation in handheld laser speckle contrast imaging

Significance

In handheld laser speckle contrast imaging (LSCI), motion artifacts (MA) are inevitable. Suppression of MA leads to a valid and objective assessment of tissue perfusion in a wide range of medical applications including dermatology and burns. Our study shines light on the sources of these artifacts, which have not yet been explored. We propose a model based on optical Doppler effect to predict speckle contrast drop as an indication of MA.

Aim

We aim to theoretically model MA when an LSCI system measuring on static scattering media is subject to translational displacements. We validate the model using both simulation and experiments. This is the crucial first step toward creating robustness against MA.

Approach

Our model calculates optical Doppler shifts in order to predict intensity correlation function and contrast of the time-integrated intensity as functions of applied speed based on illumination and detection wavevectors. To validate the theoretical predictions, computer simulation of the dynamic speckles has been carried out. Then experiments are performed by both high-speed and low-framerate imaging. The employed samples for the experiments are a highly scattering matte surface and a Delrin plate of finite scattering level in which volume scattering occurs.

Results

An agreement has been found between theoretical prediction, simulation, and experimental results of both intensity correlation functions and speckle contrast. Coefficients in the proposed model have been linked to the physical parameters according to the experimental setups.

Conclusions

The proposed model provides a quantitative description of the influence of the types of illumination and media in the creation of MA. The accurate prediction of MA caused by translation based on Doppler shifts makes our model suitable to study the influence of rotation. Also the model can be extended for the case of dynamic media, such as live tissue.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours-A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation

OBJECTIVE

Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge.

METHODS

We hereby employed "subspace approximation with augmented kernels (Saak) transform" for accurate and efficient quantification of the light-sheet image stacks following chemotherapy-treatment. We established a machine learning framework with augmented kernels based on the Karhunen-Loeve Transform (KLT) to preserve linearity and reversibility of rectification.

RESULTS

The Saak transform-based machine learning enhances computational efficiency and obviates iterative optimization of cost function needed for neural networks, minimizing the number of training datasets for segmentation in our scenario. The integration of forward and inverse Saak transforms can also serve as a light-weight module to filter adversarial perturbations and reconstruct estimated images, salvaging robustness of existing classification methods. The accuracy and robustness of the Saak transform are evident following the tests of dice similarity coefficients and various adversary perturbation algorithms, respectively. The addition of edge detection further allows for quantifying the surface area to volume ratio (SVR) of the myocardium in response to chemotherapy-induced cardiac remodeling.

CONCLUSION

The combination of Saak transform, random forest, and edge detection augments segmentation efficiency by 20-fold as compared to manual processing.

SIGNIFICANCE

This new methodology establishes a robust framework for post light-sheet imaging processing, and creating a data-driven machine learning for automated quantification of cardiac ultra-structure.

Characterisation of black skin stratum corneum by digital macroscopic images analysis

Black skin medical images generally show very low contrast. Being in a global initiative of characterisation of black skin horny layer (stratum corneum) by digital images analysis, the authors in this study proposed a four-step approach. The first step consists of differentiation between probable healthy skin regions and those affected. For that, they used an automatic classification system based on multilayer perceptron artificial neural networks. The network has been trained with texture and colour features. Best features selection and network architecture definition were done using sequential network construction algorithm-based method. After classification, selected regions undergo a colour transformation, in order to increase the contrast with the lesion region. Thirdly, created colour information serves as the basis for a modified fuzzy c-mean clustering algorithm to perform segmentation. The proposed method, named neural network-based fuzzy clustering, was applied to many black skin lesion images and they obtained segmentation rates up to 94.67%. The last stage consists in calculating characteristics. Eight parameters are concerned: uniformity, standard deviation, skewness, kurtosis, smoothness, entropy, and average pixel values calculated for red and blue colour channels. All developed methods were tested with a database of 600 images and obtained results were discussed and compared with similar works.

Throughput-Speed Product Augmentation for Scanning Fiber-Optic Two-Photon Endomicroscopy

Compactness, among several others, is one unique and very attractive feature of a scanning fiber-optic two-photon endomicroscope. To increase the scanning area and the total number of resolvable pixels (i.e., the imaging throughput), it typically requires a longer cantilever which, however, leads to a much undesired, reduced scanning speed (and thus imaging frame rate). Herein we introduce a new design strategy for a fiber-optic scanning endomicroscope, where the overall numerical aperture (NA) or beam focusing power is distributed over two stages: 1) a mode-field focuser engineered at the tip of a double-clad fiber (DCF) cantilever to pre-amplify the single-mode core NA, and 2) a micro objective of a lower magnification (i.e.,  ∼ 2× in this design) to achieve final tight beam focusing. This new design enables either an ~9-fold increase in imaging area (throughput) or an ~3-fold improvement in imaging frame rate when compared to traditional fiber-optic endomicroscope designs. The performance of an as-designed endomicroscope of an enhanced throughput-speed product was demonstrated by two representative applications: (1) high-resolution imaging of an internal organ (i.e., mouse kidney) in vivo over a large field of view without using any fluorescent contrast agents, and (2) real-time neural imaging by visualizing dendritic calcium dynamics in vivo with sub-second temporal resolution in GCaMP6m-expressing mouse brain. This cascaded NA amplification strategy is universal and can be readily adapted to other types of fiber-optic scanners in compact linear or nonlinear endomicroscopes.

Upconversion nanotubes with tunable fluorescence properties based on Gd2O2S:Ln3+ (Ln3+ = Yb3+, Er3+) and derivatives for photodynamic therapy

In this study, Gd2O2S:Ln3+ (Ln3+ = Yb3+, Er3+) upconversion nanotubes (UCNTs) were synthesised by using Gd(OH)3:Ln3+ (Ln3+ = Yb3+, Er3+) nanotubes as the template. The luminescent and biological properties of Gd2O2S:Ln3+ (Ln3+ = Yb3+, Er3+) UCNTs, along with photodynamic therapy (PDT) applications of the Gd2O2S:8%Yb3+,2%Er3+ UCNT-Ce6 (chlorin e6) nanocomposites, were systematically studied. The resultant UCNTs showed excellent biocompatibility with human retinal pigment cells (ARPE-19) even after a prolonged incubation time of 72 h, and could be used as luminescent probes. Microscopic imaging revealed that the UCNTs existed mainly in cytoplasm. PDT studies on the Gd2O2S:8%Yb3+,2%Er3+ UCNT-Ce6 nanocomposites indicate that the growth of the tumour (cell) could be inhibited dramatically when it was injected (incubated) with Gd2O2S:8%Yb3+,2%Er3+ UCNT-Ce6 nanocomposites under the irradiation of 980 nm laser.

An artificial intelligent platform for live cell identification and the detection of cross-contamination

BACKGROUND

About 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images.

METHODS

Seven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling. About 2 million patch images were used for model training and testing. AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification. To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the cell semantic segmentation was conducted by DilatedNet.

RESULTS

The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%.

CONCLUSIONS

The deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Learning colon centreline from optical colonoscopy, a new way to generate a map of the internal colon surface

Optical colonoscopy is known as a gold standard screening method in detecting and removing cancerous polyps. During this procedure, some polyps may be undetected due to their positions, not being covered by the camera or missed by the surgeon. In this Letter, the authors introduce a novel convolutional neural network (ConvNet) algorithm to map the internal colon surface to a 2D map (visibility map), which can be used to increase the awareness of clinicians about areas they might miss. This was achieved by leveraging a colonoscopy simulator to generate a dataset consisting of colonoscopy video frames and their corresponding colon centreline (CCL) points in 3D camera coordinates. A pair of video frames were used as input to a ConvNet, whereas the output was a point on the CCL and its direction vector. By knowing CCL for each frame and roughly modelling the colon as a cylinder, frames could be unrolled to build a visibility map. They validated their results using both simulated and real colonoscopy frames. Their results showed that using consecutive simulated frames to learn the CCL can be generalised to real colonoscopy video frames to generate a visibility map.

Determining blood flow direction from short neurovascular surgical microscope videos

Neurovascular surgery aims to repair diseased or damaged blood vessels in the brain or spine. There are numerous procedures that fall under this category, and in all of them, the direction of blood flow through these vessels is crucial information. Current methods to determine this information intraoperatively include static pre-operative images combined with augmented reality, Doppler ultrasound, and injectable fluorescent dyes. Each of these systems has inherent limitations. This study includes the proposal and preliminary validation of a technique to identify the direction of blood flow through vessels using only video segments of a few seconds acquired from routinely used surgical microscopes. The video is enhanced to reveal subtle colour fluctuations related to blood pulsation, and these rhythmic signals are further analysed in Fourier space to reveal the direction of blood flow. The proposed method was validated using a novel physical phantom and retrospective analysis of surgical videos and demonstrated high accuracy in identifying the direction of blood flow.

Endoscopic video defogging using luminance blending

Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475.

Upper gastrointestinal anatomy detection with multi-task convolutional neural networks

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors’ model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.

Marker-less real-time intra-operative camera and hand-eye calibration procedure for surgical augmented reality

Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.

Highly efficient polyethylene glycol-functionalised gold nanorods for photothermal ablation of hepatocellular carcinoma cells

Gold nanorods (GNRs) with exceptional photothermal properties have held promising potential for application in the biomedical field. In this study, the authors achieved photothermal ablation by polyethylene glycol (PEG)-functionalised GNRs. Well-dispersed and uniform GNRs were produced through a seed-mediated growth method. A thermal camera was used to scrutinise the temperature distribution and efficiency of the photothermal properties of the GNRs, which were irradiated by an 808 nm laser on a silicon chip. They observed that the GNRs provided about a 5°C temperature increase and produced hyperthermia efficiently. Since GNRs need to be surface tailored with a biocompatible material rather than cetyltrimethylammonium bromide (CTAB), they chose methoxyl PEG thiol to modify the GNRs. By taking advantage of the alkaline environment that assists this functionalisation, they accomplished about 89% removal of CTAB and identified a PEG layer on the surface of the GNRs. The GNR biocompatibility was considerably improved without any shift of the optical properties. Hepatocellular carcinoma cells were incubated with GNRs for 24 h and then were irradiated with a near-infrared laser for 3 min. Few cells remained alive, which demonstrated the photothermal ablation ability of the GNRs.

Trastuzumab-conjugated nanoparticles composed of poly(butylene adipate-co-butylene terephthalate) prepared by electrospraying technique for targeted delivery of docetaxel

Human epidermal growth factor receptor 2 (HER-2) is overexpressed in 20-30% of human breast cancers, associated with poor prognosis and tumour aggression. The aim of this study was the production of trastuzumab-targeted Ecoflex nanoparticles (NPs) loaded with docetaxel and in vitro evaluation of their cytotoxicity and cellular uptake. The NPs were manufactured by electrospraying and characterised regarding size, zeta potential, drug loading, and release behaviour. Then their cytotoxicity was evaluated by MTT assay against an HER-2-positive cell line, BT-474, and an HER-2-negative cell line, MDA-MB-468. The cellular uptake was studied by flow cytometry and fluorescent microscope. The particle size of NPs was in an appropriate range, with relatively high drug entrapment and acceptable release efficiency. The results showed no cytotoxicity for the polymer, but the significant increment of cytotoxicity was observed by treatment with docetaxel-loaded NPs in both HER-2-positive and HER-2-negative cell lines, in comparison with the free drug. The trastuzumab-targeted NPs also significantly enhanced cytotoxicity against BT-474 cells, compared with non-targeted NPs.

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.

Nephron ultrastructural alterations induced by zinc oxide nanoparticles: an electron microscopic study

Due to their unique properties, zinc oxide nanoparticles (ZnO NPs) are invested in many industries, commercial products, and nanomedicine with potential risk for human health and the environment. The present study aims to focus on alterations that might be induced by ZnO NPs in the nephron ultrastructure. Male Wister Albino rats were subjected to ZnO NPs at a daily dose of 2 mg/kg for 21 days. Kidney biopsies were processed to transmission electron microscopy (TEM) and ultrastructural pathology examinations. Exposure to ZnO NPs‐induced ultrastructural alterations in the proximal convoluted tubules (PCTs) and to lesser extent in the distal ones (DCTs), while the loops of Henle were almost not affected. The glomeruli demonstrated dilatation, partial mesangial cells loss, matrix ballooning, slits filtration widening, and basement membrane thickening. Moreover, PCT revealed cytoplasmic necrosis, vacuolation, erosion, and disorganisation of the apical microvilli together with mitochondrial swelling and cristae destruction. The nuclei of the renal cells exhibited nuclear deformity, heterochromatin accumulation, and apoptotic activities. The findings indicate that ZnO nanomaterial have the potential to affect the nephron ultrastructure suggesting alteration in the kidney functions. More work is needed for better understanding the toxicity and pathogenesis of ZnO oxide nanomaterial.

Custom Scanning Hyperspectral Imaging System for Biomedical Applications: Modeling, Benchmarking, and Specifications

Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

Near-Infrared-Fluorescent Erythrocyte-Mimicking Particles: Physical and Optical Characteristics

Exogenous fluorescent materials activated by near-infrared (NIR) light can offer deep optical imaging with subcellular resolution, and enhanced image contrast. We have engineered NIR particles by doping hemoglobin-depleted erythrocyte ghosts (EGs) with indocyanine green (ICG). We refer to these optical particles as NIR erythrocyte-mimicking transducers (NETs). A particular feature of NETs is that their diameters can be tuned from micrometer to nanometer scale, thereby, providing a capability for broad NIR biomedical imaging applications. Herein, we investigate the effects of ICG concentration on key material properties of micrometer-sized NETs, and nanometer-sized NETs fabricated by either sonication or mechanical extrusion of EGs. The zeta potentials of NETs do not vary significantly with ICG concentration, suggesting that ICG is encapsulated within NETs regardless of particle size or ICG concentration. Loading efficiency of ICG into the NETs monotonically decreases with increasing values of ICG concentration. Based on quantitative analyses of the fluorescence emission spectra of the NETs, we determine that 20 μM ICG utilized during fabrication of NETs presents an optimal concentration that maximizes the integrated fluorescence emission for micrometer- and nanometer-sized NETs. Encapsulation of the ICG in these constructs also enhances the fluorescence stability and quantum yield of ICG. These results guide the engineering of NETs with maximal NIR emission for imaging applications such as fluorescence-guided tumor resection and real-time angiography.

Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some⁻Different⁻Light into the Dark

Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the potential use of these technologies in the medical domain, with emphasis in the current advances in gastroenterology. The main aim of this review is to provide an overview of contemporary concepts regarding HSI technology together with state-of-art systems and applications in gastroenterology. Finally, we discuss the current limitations and upcoming trends of HSI in gastroenterology.

Endoscopic image enhancement with noise suppression

Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insufficient and irregular light sources become major challenges for endoscopic surgery. Not only do these conditions hinder image processing algorithms, sometimes surgical tools are barely visible when operating within low-light regions. In addition, low-light regions have low signal-to-noise ratio and metrication artefacts due to quantisation errors. As a result, present image enhancement methods usually suffer from heavy noise amplification in low-light regions. In this Letter, the authors propose an effective method for endoscopic image enhancement by identifying different illumination regions and designing the enhancement design criteria for desired image quality. Compared with existing image enhancement methods, the proposed method is able to enhance the low-light region while preventing noise amplification during image enhancement process. The proposed method is tested with 200 images acquired by endoscopic surgeries. Computed results show that the proposed algorithm can outperform state-of-the-art algorithms for image enhancement, in terms of naturalness image quality evaluator and illumination index.

Remote examination of exudates-impact of macular oedema

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

Microscopic investigation of" topically applied nanoparticles for molecular imaging of fresh tissue surfaces

Previous studies have shown that functionalized nanoparticles (NPs) topically applied on fresh tissues are able to rapidly target cell-surface protein biomarkers of cancer. Furthermore, studies have shown that a paired-agent approach, in which an untargeted NP is co-administered with a panel of targeted NPs, controls for the nonspecific behavior of the NPs, enabling quantitative imaging of biomarker expression. However, given the complexities in nonspecific accumulation, diffusion, and chemical binding of targeted NPs in tissues, studies are needed to better understand these processes at the microscopic scale. Here, fresh tissues were stained with a paired-agent approach, frozen, and sectioned to image the depth-dependent accumulation of targeted and untargeted NPs. The ratio of targeted-to-untargeted NP concentrations-a parameter used to distinguish between tumor and benign tissues-was found to diminish with increasing NP diffusion depths due to nonspecific accumulation and poor washout. It was then hypothesized and experimentally demonstrated that larger NPs would exhibit less diffusion below tissue surfaces, enabling higher targeted-to-untargeted NP ratios. In summary, these methods and investigations have enabled the design of NP agents with improved sensitivity and contrast for rapid molecular imaging of fresh tissues.

Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Distance sonification in image-guided neurosurgery

Image-guided neurosurgery, or neuronavigation, has been used to visualise the location of a surgical probe by mapping the probe location to pre-operative models of a patient's anatomy. One common limitation of this approach is that it requires the surgeon to divert their attention away from the patient and towards the neuronavigation system. In order to improve this type of application, the authors designed a system that sonifies (i.e. provides audible feedback of) distance information between a surgical probe and the location of the anatomy of interest. A user study (n = 15) was completed to determine the utility of sonified distance information within an existing neuronavigation platform (Intraoperative Brain Imaging System (IBIS) Neuronav). The authors' results were consistent with the idea that combining auditory distance cues with existing visual information from image-guided surgery systems may result in greater accuracy when locating specified points on a pre-operative scan, thereby potentially reducing the extent of the required surgical openings, as well as potentially increasing the precision of individual surgical tasks. Further, the authors' results were also consistent with the hypothesis that combining auditory and visual information reduces the perceived difficulty in locating a target location within a three-dimensional volume.

Estimation of surgical tool-tip tracking error distribution in coordinate reference frame involving pivot calibration uncertainty

Accurate understanding of surgical tool-tip tracking error is important for decision making in image-guided surgery. In this Letter, the authors present a novel method to estimate/model surgical tool-tip tracking error in which they take pivot calibration uncertainty into consideration. First, a new type of error that is referred to as total target registration error (TTRE) is formally defined in a single-rigid registration. Target localisation error (TLE) in two spaces to be registered is considered in proposed TTRE formulation. With first-order approximation in fiducial localisation error (FLE) or TLE magnitude, TTRE statistics (mean, covariance matrix and root-mean-square (RMS)) are then derived. Second, surgical tool-tip tracking error in optical tracking system (OTS) frame is formulated using TTRE when pivot calibration uncertainty is considered. Finally, TTRE statistics of tool-tip in OTS frame are then propagated relative to a coordinate reference frame (CRF) rigid-body. Monte Carlo simulations are conducted to validate the proposed error model. The percentage passing statistical tests that there is no difference between simulated and theoretical mean and covariance matrix of tool-tip tracking error in CRF space is more than 90% in all test cases. The RMS percentage difference between simulated and theoretical tool-tip tracking error in CRF space is within 5% in all test cases.

Facile one‐pot hydrothermal synthesis of stable and biocompatible fluorescent carbon dots from lemon grass herb

Luminescent carbon‐based nanomaterials hold great promise due to their stable photo‐physical behaviour, biocompatibility and lower toxicity. This work involves economic and facile one‐pot green synthesis of water‐soluble nanostructures from lemon grass (LGNS) [Cymbopogon citratus (DC) Stapf ] as carbon source. High‐resolution transmission electron microscopy confirmed the formation of LGNS with lattice spacing of 0.23 nm matching low‐dimensional graphitic structures. The strong absorption exhibited at 278 nm could be attributed to л‐states of sp2 /sp3 hybridisation in carbon nanostructures. Fluorescence spectroscopy of LGNS exhibited strong excitation‐dependent emission properties over a broad range of wavelengths from 300 to 600 nm. Quantitatively, these LGNS were estimated to have quantum yield of 23.3%. Biomass derived LGNS could be potentially exploited for wide variety of applications like bioimaging, up‐conversion, drug delivery and optoelectronic devices. To this extent, synthesised LGNS were used to image yeast cells via multicolour/multi‐excitation fluorescence imaging.

Real-time geometry-aware augmented reality in minimally invasive surgery

The potential of augmented reality (AR) technology to assist minimally invasive surgery (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-time and accurate augmented information overlay in MIS is still a formidable task. In this Letter, the authors present a novel real-time AR framework for MIS that achieves interactive geometric aware AR in endoscopic surgery with stereo views. The authors' framework tracks the movement of the endoscopic camera and simultaneously reconstructs a dense geometric mesh of the MIS scene. The movement of the camera is predicted by minimising the re-projection error to achieve a fast tracking performance, while the three-dimensional mesh is incrementally built by a dense zero mean normalised cross-correlation stereo-matching method to improve the accuracy of the surface reconstruction. The proposed system does not require any prior template or pre-operative scan and can infer the geometric information intra-operatively in real time. With the geometric information available, the proposed AR framework is able to interactively add annotations, localisation of tumours and vessels, and measurement labelling with greater precision and accuracy compared with the state-of-the-art approaches.

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

Design and evaluation of an augmented reality simulator using leap motion

Advances in virtual and augmented reality (AR) are having an impact on the medical field in areas such as surgical simulation. Improvements to surgical simulation will provide students and residents with additional training and evaluation methods. This is particularly important for procedures such as the endoscopic third ventriculostomy (ETV), which residents perform regularly. Simulators such as NeuroTouch, have been designed to aid in training associated with this procedure. The authors have designed an affordable and easily accessible ETV simulator, and compare it with the existing NeuroTouch for its usability and training effectiveness. This simulator was developed using Unity, Vuforia and the leap motion (LM) for an AR environment. The participants, 16 novices and two expert neurosurgeons, were asked to complete 40 targeting tasks. Participants used the NeuroTouch tool or a virtual hand controlled by the LM to select the position and orientation for these tasks. The length of time to complete each task was recorded and the trajectory log files were used to calculate performance. The resulting data from the novices' and experts' speed and accuracy are compared, and they discuss the objective performance of training in terms of the speed and accuracy of targeting accuracy for each system.

Applicability of quantitative optical imaging techniques for intraoperative perfusion diagnostics: a comparison of laser speckle contrast imaging, sidestream dark-field microscopy, and optical coherence tomography

Patient morbidity and mortality due to hemodynamic complications are a major problem in surgery. Optical techniques can image blood flow in real-time and high-resolution, thereby enabling perfusion monitoring intraoperatively. We tested the feasibility and validity of laser speckle contrast imaging (LSCI), optical coherence tomography (OCT), and sidestream dark-field microscopy (SDF) for perfusion diagnostics in a phantom model using whole blood. Microvessels with diameters of 50, 100, and 400  μm were constructed in a scattering phantom. Perfusion was simulated by pumping heparinized human whole blood at five velocities (0 to 20  mm/s). Vessel diameter and blood flow velocity were assessed with LSCI, OCT, and SDF. Quantification of vessel diameter was feasible with OCT and SDF. LSCI could only visualize the 400-μm vessel, perfusion units scaled nonlinearly with blood velocity. OCT could assess blood flow velocity in terms of inverse OCT speckle decorrelation time. SDF was not feasible to measure blood flow; however, for diluted blood the measurements were linear with the input velocity up to 1  mm/s. LSCI, OCT, and SDF were feasible to visualize blood flow. Validated blood flow velocity measurements intraoperatively in the desired parameter (mL·min-1·g-1) remain challenging.

New Endoscopic Imaging Technology Based on MEMS Sensors and Actuators

Over the last decade, optical fiber-based forms of microscopy and endoscopy have extended the realm of applicability for many imaging modalities. Optical fiber-based imaging modalities permit the use of remote illumination sources and enable flexible forms supporting the creation of portable and hand-held imaging instrumentations to interrogate within hollow tissue cavities. A common challenge in the development of such devices is the design and integration of miniaturized optical and mechanical components. Until recently, microelectromechanical systems (MEMS) sensors and actuators have been playing a key role in shaping the miniaturization of these components. This is due to the precision mechanics of MEMS, microfabrication techniques, and optical functionality enabling a wide variety of movable and tunable mirrors, lenses, filters, and other optical structures. Many promising results from MEMS based optical fiber endoscopy have demonstrated great potentials for clinical translation. In this article, reviews of MEMS sensors and actuators for various fiber-optical endoscopy such as fluorescence, optical coherence tomography, confocal, photo-acoustic, and two-photon imaging modalities will be discussed. This advanced MEMS based optical fiber endoscopy can provide cellular and molecular features with deep tissue penetration enabling guided resections and early cancer assessment to better treatment outcomes.

High-frequency-based features for low and high retina haemorrhage classification

Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). The obtained experimental results show that statistical features obtained by EMD, VMD, and DWT, respectively, achieved 88.31% ± 0.0832, 71% ± 0.1782, and 64% ± 0.0949 accuracies. It also outperformed VMD and DWT in terms of sensitivity and specificity. Thus, the EMD-based features are promising for grading retinal HAs.

Multiplexed Molecular Imaging of Fresh Tissue Surfaces Enabled by Convection-Enhanced Topical Staining with SERS-Coded Nanoparticles

There is a need for intraoperative imaging technologies to guide breast-conserving surgeries and to reduce the high rates of re-excision for patients in which residual tumor is found at the surgical margins during postoperative pathology analyses. Feasibility studies have shown that utilizing topically applied surface-enhanced Raman scattering (SERS) nanoparticles (NPs), in conjunction with the ratiometric imaging of targeted versus untargeted NPs, enables the rapid visualization of multiple cell-surface biomarkers of cancer that are overexpressed at the surfaces of freshly excised breast tissues. In order to reliably and rapidly perform multiplexed Raman-encoded molecular imaging of large numbers of biomarkers (with five or more NP flavors), an enhanced staining method has been developed in which tissue surfaces are cyclically dipped into an NP-staining solution and subjected to high-frequency mechanical vibration. This dipping and mechanical vibration (DMV) method promotes the convection of the SERS NPs at fresh tissue surfaces, which accelerates their binding to their respective biomarker targets. By utilizing a custom-developed device for automated DMV staining, this study demonstrates the ability to simultaneously image four cell-surface biomarkers of cancer at the surfaces of fresh human breast tissues with a mixture of five flavors of SERS NPs (four targeted and one untargeted control) topically applied for 5 min and imaged at a spatial resolution of 0.5 mm and a raster-scanned imaging rate of >5 cm² min^-1 .

Surgical Guidance via Multiplexed Molecular Imaging of Fresh Tissues Labeled with SERS-Coded Nanoparticles

The imaging of dysregulated cell-surface receptors (or biomarkers) is a potential means of identifying the presence of cancer with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Recently, surface-enhanced Raman-scattering (SERS) nanoparticles (NPs) have attracted wide interest due to their potential for sensitive and multiplexed biomarker detection. In this review, we focus on the most recent advances in tumor imaging using SERS-coded NPs. A brief introduction of the structure and optical properties of SERS NPs is provided, followed by a detailed discussion of key imaging issues such as the administration of NPs in tissue (topical versus systemic), the optical configuration and imaging approach of Raman imaging systems, spectral demultiplexing methods for quantifying NP concentrations, and the disambiguation of specific vs. nonspecific sources of contrast through ratiometric imaging of targeted and untargeted (control) NP pairs. Finally, future challenges and directions are briefly outlined.

The Role of Laser Speckle Imaging in Port-Wine Stain Research: Recent Advances and Opportunities

Here, we review our current knowledge on the etiology and treatment of port-wine stain (PWS) birthmarks. Current treatment options have significant limitations in terms of efficacy. With the combination of 1) a suitable preclinical microvascular model, 2) laser speckle imaging (LSI) to evaluate blood-flow dynamics, and 3) a longitudinal experimental design, rapid preclinical assessment of new phototherapies can be translated from the lab to the clinic. The combination of photodynamic therapy (PDT) and pulsed-dye laser (PDL) irradiation achieves a synergistic effect that reduces the required radiant exposures of the individual phototherapies to achieve persistent vascular shutdown. PDL combined with anti-angiogenic agents is a promising strategy to achieve persistent vascular shutdown by preventing reformation and reperfusion of photocoagulated blood vessels. Integration of LSI into the clinical workflow may lead to surgical image guidance that maximizes acute photocoagulation, is expected to improve PWS therapeutic outcome. Continued integration of noninvasive optical imaging technologies and biochemical analysis collectively are expected to lead to more robust treatment strategies.

Computed Optical Interferometric Imaging: Methods, Achievements, and Challenges

Three-dimensional high-resolution optical imaging systems are generally restricted by the trade-off between resolution and depth-of-field as well as imperfections in the imaging system or sample. Computed optical interferometric imaging is able to overcome these longstanding limitations using methods such as interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) which manipulate the complex interferometric data. These techniques correct for limited depth-of-field and optical aberrations without the need for additional hardware. This paper aims to outline these computational methods, making them readily available to the research community. Achievements of the techniques will be highlighted, along with past and present challenges in implementing the techniques. Challenges such as phase instability and determination of the appropriate aberration correction have been largely overcome so that imaging of living tissues using ISAM and CAO is now possible. Computed imaging in optics is becoming a mature technology poised to make a significant impact in medicine and biology.

Colour-reproduction algorithm for transmitting variable video frames and its application to capsule endoscopy

Presented is a new power-efficient colour generation algorithm for wireless capsule endoscopy (WCE) application. In WCE, transmitting colour image data from the human intestine through radio frequency (RF) consumes a huge amount of power. The conventional way is to transmit all R, G and B components of all frames. Using the proposed dictionary-based colour generation scheme, instead of sending all R, G and B frames, first one colour frame is sent followed by a series of grey-scale frames. At the receiver end, the colour information is extracted from the colour frame and then added to colourise the grey-scale frames. After a certain number of grey-scale frames, another colour frame is sent followed by the same number of grey-scale frames. This process is repeated until the end of the video sequence to maintain the colour similarity. As a result, over 50% of RF transmission power can be saved using the proposed scheme, which will eventually lead to a battery life extension of the capsule by 4-7 h. The reproduced colour images have been evaluated both statistically and subjectively by professional gastroenterologists. The algorithm is finally implemented using a WCE prototype and the performance is validated using an ex-vivo trial.

Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results

Both the theoretical background and the experimental results of an algorithm developed to perform human respiratory rate measurements without any physical contact are presented. Based on depth image sensing techniques, the respiratory rate is derived by measuring morphological changes of the chest wall. The algorithm identifies the human chest, computes its distance from the camera and compares this value with the instantaneous distance, discerning if it is due to the respiratory act or due to a limited movement of the person being monitored. To experimentally validate the proposed algorithm, the respiratory rate measurements coming from a spirometer were taken as a benchmark and compared with those estimated by the algorithm. Five tests were performed, with five different persons sat in front of the camera. The first test aimed to choose the suitable sampling frequency. The second test was conducted to compare the performances of the proposed system with respect to the gold standard in ideal conditions of light, orientation and clothing. The third, fourth and fifth tests evaluated the algorithm performances under different operating conditions. The experimental results showed that the system can correctly measure the respiratory rate, and it is a viable alternative to monitor the respiratory activity of a person without using invasive sensors.

Continuous non-contact vital sign monitoring in neonatal intensive care unit

Current technologies to allow continuous monitoring of vital signs in pre-term infants in the hospital require adhesive electrodes or sensors to be in direct contact with the patient. These can cause stress, pain, and also damage the fragile skin of the infants. It has been established previously that the colour and volume changes in superficial blood vessels during the cardiac cycle can be measured using a digital video camera and ambient light, making it possible to obtain estimates of heart rate or breathing rate. Most of the papers in the literature on non-contact vital sign monitoring report results on adult healthy human volunteers in controlled environments for short periods of time. The authors' current clinical study involves the continuous monitoring of pre-term infants, for at least four consecutive days each, in the high-dependency care area of the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital in Oxford. The authors have further developed their video-based, non-contact monitoring methods to obtain continuous estimates of heart rate, respiratory rate and oxygen saturation for infants nursed in incubators. In this Letter, it is shown that continuous estimates of these three parameters can be computed with an accuracy which is clinically useful. During stable sections with minimal infant motion, the mean absolute error between the camera-derived estimates of heart rate and the reference value derived from the ECG is similar to the mean absolute error between the ECG-derived value and the heart rate value from a pulse oximeter. Continuous non-contact vital sign monitoring in the NICU using ambient light is feasible, and the authors have shown that clinically important events such as a bradycardia accompanied by a major desaturation can be identified with their algorithms for processing the video signal.

User-guided segmentation for volumetric retinal optical coherence tomography images

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Volumetric in vivo imaging of microvascular perfusion within the intact cochlea in mice using ultra-high sensitive optical microangiography

Studying the inner ear microvascular dynamics is extremely important to understand the cochlear function and to further advance the diagnosis, prevention, and treatment of many otologic disorders. However, there is currently no effective imaging tool available that is able to access the blood flow within the intact cochlea. In this paper, we report the use of an ultrahigh sensitive optical micro-angiography (UHS-OMAG) imaging system to image 3-D microvascular perfusion within the intact cochlea in living mice. The UHS-OMAG image system used in this study is based on spectral domain optical coherence tomography, which uses a broadband light source centered at 1300 nm with an imaging rate of 47[Formula: see text] 000 A-scans/s, capable of acquiring high-resolution B scans at 300 frames/s. The technique is sensitive enough to image very slow blood flow velocities, such as those found in capillary networks. The 3-D imaging acquisition time for a whole cochlea is  ∼ 4.1 s. We demonstrate that volumetric reconstruction of microvascular flow obtained by UHS-OMAG provides a comprehensive perfusion map of several regions of the cochlea, including the otic capsule, the stria vascularis of the apical and middle turns and the radiating arterioles that emanate from the modiolus.

Monte Carlo simulation of the spatial resolution and depth sensitivity of two-dimensional optical imaging of the brain

Absorption or fluorescence-based two-dimensional (2-D) optical imaging is widely employed in functional brain imaging. The image is a weighted sum of the real signal from the tissue at different depths. This weighting function is defined as "depth sensitivity." Characterizing depth sensitivity and spatial resolution is important to better interpret the functional imaging data. However, due to light scattering and absorption in biological tissues, our knowledge of these is incomplete. We use Monte Carlo simulations to carry out a systematic study of spatial resolution and depth sensitivity for 2-D optical imaging methods with configurations typically encountered in functional brain imaging. We found the following: (i) the spatial resolution is <200 μm for NA≤0.2 or focal plane depth≤300 μm. (ii) More than 97% of the signal comes from the top 500 μm of the tissue. (iii) For activated columns with lateral size larger than spatial resolution, changing numerical aperature (NA) and focal plane depth does not affect depth sensitivity. (iv) For either smaller columns or large columns covered by surface vessels, increasing NA and/or focal plane depth may improve depth sensitivity at deeper layers. Our results provide valuable guidance for the optimization of optical imaging systems and data interpretation.

Endoscopic Functional Fourier Domain Common Path Optical Coherence Tomography for Microsurgery

A single-arm interferometer based optical coherence tomography (OCT) system known as common-path OCT (CPOCT) is rapidly progressing towards practical application. Due in part to the simplicity and robustness of its design, Fourier Domain CPOCT (FD-CP-OCT) offers advantages in many endoscopic sensing and imaging applications. FD-CP-OCT uses simple, interchangeable fiber optic probes that are easily integrated into small and delicate surgical tools. The system is capable of providing not only high resolution imaging but also optical sensing. Here, we report progress towards practical application of FD-CP-OCT in the setting of delicate microsurgical procedures such as intraocular retinal surgery. To meet the challenges presented by the microsurgical requirements of these procedures, we have developed and initiated the validation of applicable fiber optic probes. By integrating these probes into our developing imaging system, we have obtained high resolution OCT images and have also completed a demonstration of their potential sensing capabilities. Specifically, we utilize multiple SLEDs to demonstrate sub 3-micron axial resolution in water; we propose a technique to quantitatively evaluate the spatial distribution of oxygen saturation levels in tissue; and we present evidence supportive of the technology's surface sensing and tool guidance potential by demonstrating topological and motion compensation capabilities.

LED-Based Optical Device for Chronic In Vivo Cerebral Blood Volume Measurement

We demonstrate a reflectivity-based cerebral blood volume sensor comprised of surface-mount light-emitting diodes on a flexible substrate with integrated photodetectors in a form factor suitable for direct brain contact and chronic implantation. This reflectivity monitor is able to measure blood flow through the change of the surface reflectivity and, through this mechanism, detect the cerebral-blood-volume changes associated with epileptic seizures with a signal-to-noise (SNR) response of 42 dB. The device is tested in an in vivo model confirming its compatibility and sensitivity. The data taken demonstrate that placing the sensor into direct brain contact improves the SNR by more than four orders of magnitude over current noncontact technologies.

Is thermal scanner losing its bite in mass screening of fever due to SARS?

Severe acute respiratory syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect a potential SARS infected person plays an important role in preventing the spread of SARS. The use of infrared thermal imaging cameras has been proposed as a noninvasive, speedy, cost effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras can potentially be used to detect subjects with fever, the cardinal symptom of SARS, and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the core body temperature. As known, the body determines a temperature as its so-called "set point" at any one time during the body temperature regulation. Fever happens if the hypothalamus detects pyrogens and then raises the set point. The time course of a typical fever can be divided into three stages. When the fever initiates, the body attempts to raise its temperature but vasoconstriction occurs to prevent heat loss through the skin. With this reason, some individuals at this stage of fever (at the rising slope and immediately after fever begins or falling slope after the fever breaks) will not be detected by the scanner if it is not designed to detect subject at the plateau of the fever (with her/his high core temperature) in particular. This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases, improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Various statistical methods such as linear regression, Receiver Operating Characteristics analysis, and neural networks based classification were used to analyze the temperature data collected from various sites on the face on both the frontal and side profiles. Two important conclusions were drawn from the analysis: the best region on the face to obtain temperature readings and the optimal preset threshold temperature for the thermal imager. To conclude, the current research application will remain an interest and useful for reference by both local and overseas manufacturers of thermal scanners, users, and various government and private establishments. As elevation of body temperature is a common presenting symptom for many illnesses including infectious diseases, thermal imagers are useful tools for mass screening of body temperature not only for SARS but also during other public health crisis where widespread transmission of infection is a concern.

Localization of absorbers in scattering media by use of frequency-domain measurements of time-dependent photon migration

Frequency-domain studies of time-dependent light propagation in tissuelike phantoms that contain optical heterogeneities are described. Specifically the phase shift and amplitude modulation of reemergent light were measured when illuminated by an amplitude-modulated light source. Changes in the phase angle and the extent of modulation revealed the presence of a light-absorbing object. Furthermore the magnitude and direction of these changes were sensitive to the absorber depth and the light modulation frequency in a manner that could be used to infer the location of the heterogeneity. These data suggest the feasibility of optical imaging by frequency-domain methods.