Endoscopic optical coherence tomography with a flexible fiber bundle

We demonstrate in vivo endoscopic optical coherence tomography (OCT) imaging in the forward direction using a flexible fiber bundle. In comparison to current conventional forward looking probe schemes, our approach simplifies the endoscope design by avoiding the integration of any beam steering components in the distal probe end due to 2D scanning of a focused light beam over the proximal fiber bundle surface. We describe the challenges that arise when OCT imaging with a fiber bundle is performed, such as multimoding or cross-coupling. The performance of different fiber bundles with varying parameters such as numerical aperture, core size and core structure was consequently compared and artifacts that degrade the image quality were described in detail. Based on our findings, we propose an optimal fiber bundle design for endoscopic OCT imaging

Determinants of Object Persistence: The Role of Cue Type, Cue Duration and Cue Strength

Four experiments investigated object persistence in conscious awareness as a function of the nature of the cues that permit the object to be segregated from the background, and identified. A number of factors were manipulated (cue type, [color, motion, color & motion] cue duration after object identification [1s vs 5s] and cue strength [strong vs weak]). Performance was fractionated into identification, maintenance and persistence components. The results show that (1) stronger cues yielded faster identification, and (2) persistence was independent of identification time, and (3) motion cues were associated with longer persistence than color cues. A distinction between dorsal and ventral visual pathways as used to segregate the object from the background provides one way to organize the data

Beyond backscattering: Optical neuroimaging by BRAD

Optical coherence tomography (OCT) is a powerful technology for rapid volumetric imaging in biomedicine. The bright field imaging approach of conventional OCT systems is based on the detection of directly backscattered light, thereby waiving the wealth of information contained in the angular scattering distribution. Here we demonstrate that the unique features of few-mode fibers (FMF) enable simultaneous bright and dark field (BRAD) imaging for OCT. As backscattered light is picked up by the different modes of a FMF depending upon the angular scattering pattern, we obtain access to the directional scattering signatures of different tissues by decoupling illumination and detection paths. We exploit the distinct modal propagation properties of the FMF in concert with the long coherence lengths provided by modern wavelength-swept lasers to achieve multiplexing of the different modal responses into a combined OCT tomogram. We demonstrate BRAD sensing for distinguishing differently sized microparticles and showcase the performance of BRAD-OCT imaging with enhanced contrast for ex vivo tumorous tissue in glioblastoma and neuritic plaques in Alzheimer's disease

Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions

This work is dedicated to the simulation of full cycles of the electrical activity of the heart and the corresponding body surface potential. The model is based on a realistic torso and heart anatomy, including ventricles and atria. One of the specificities of our approach is to model the atria as a surface, which is the kind of data typically provided by medical imaging for thin volumes. The bidomain equations are considered in their usual formulation in the ventricles, and in a surface formulation on the atria. Two ionic models are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin boundary condition based on a resistor- capacitor transmission condition. Various ECGs are simulated in healthy and pathological conditions (left and right bundle branch blocks, Bachmann's bundle block, Wolff-Parkinson-White syndrome). To assess the numerical ECGs, we use several qualitative and quantitative criteria found in the medical literature. Our simulator can also be used to generate the signals measured by a vest of electrodes. This capability is illustrated at the end of the article

Recognizing a Heart Attack: Patients’ Knowledge of Cardiovascular Risk Factors and Its Relation to Prehospital Decision Delay in Acute Coronary Syndrome

In acute coronary syndromes (ACSs), longer decision delay – the time patients wait before seeking medical attention after symptoms have started – increases the risk of complications and death. However, many patients wait much longer than recommended and research is needed investigating how patient decision delay can be reduced. In a cross-sectional study of 120 ACS survivors, we investigated the relationship between knowledge of cardiovascular risk factors and decision delay. Several days after the onset of a cardiac event, patients completed a questionnaire measuring demographics, decision delay, objective knowledge of cardiovascular risks factors and of ACS symptoms, and subjective perceptions of symptoms during the cardiac episode. Relevant clinical data were extracted from patients’ medical records. In a multiple linear regression analysis, controlling for demographic and clinical factors, objective knowledge of cardiovascular risk factors and ACS symptoms, and subjective attributions of symptoms to a cardiac cause were related to shorter decision delays. Among patients with relatively high knowledge of risk factors, only 5% waited more than 1 h to seek help, compared to 22% among patients with relatively low knowledge. These results suggest that knowledge of the factors that increase the risk of developing cardiovascular disease could play a role in patient decision making during an acute cardiac event. We discuss methodological issues and potential underlying mechanisms related to decision heuristics and biases, which can inform future research.Spanish Ministry of Economy, Industry, and Competitiveness PSI2011-22954 PSI2014-51842-RPlan Propio de InvestigaciónAndalusian Regional GovernmentEuropean Union (EU) SOMM17-6103-UG

Dense Optical Flow Estimation using Diffusion Distances

Diffusion maps have been shown to model relations between points by considering the overall connectivity of the graph. This report outlines how we can apply the diffusion framework to dense optical flow estimation where diffusion maps are used to embed distributions of local spatial gradients. We review the problem of dense optical flow estimation and several broad types of approaches to computing accurate estimate of the flow. We then review the diffusion framework and its predecessors in the manifold learning literature. Local image features are recorded by diffusion distances calculated from the graph Laplacian whose kernel function depends on inter-pixel intensity differences in a certain neighbourhood. These features are then used in a correlational optical flow estimation algorithm to illustrate the improvement to the dense estimate of optical flow by using a richer description of features as the elementary unit in the estimation. By considering systems of correlation vectors from image neigbourhoods, we also increase the smoothness of the estimate. The present work compares several smoothing principles, including the vector mean, vector median, marginal median which are based on both the maximum correlation and minimum rank of correlation vectors from the correlation matrix. A large number of very accurate estimates, spread through the image can be identified based on level of consensus with the estimates from surrounding pixels, which we term as confidence. We use this confidence information as a basis for smoothing the motion estimate by filling regions with poor confidence with estimates from neighboring high confidence regions. The proposed methodology was applied on two distinct image sequences from the Middlebury data set, as well as a fluid motion data set. Results show the robustness of our method to the different types of input data