Liquid-crystal based drift-free polarization modulators: Part II. Ultra-stable Stokes and Mueller polarimeters.

We have previously reported a new design for drift-free liquid-crystal polarization modulators (LCMs) based on liquid-crystal variable retarders (LCVRs). Here, we study their performance on Stokes and Mueller polarimeters. LCMs have polarimetric responses similar to LCVRs and can be used as temperature-stable alternatives to many LCVR-based polarimeters. We have built an LCM-based polarization state analyzer (PSA) and compared its performance to an equivalent LCVR-based PSA. Our system parameters remained stable over a wide range of temperature, precisely from 25°C to 50°C. Accurate Stokes and Mueller measurements have been conducted, paving the way to calibration-free polarimeters for demanding applications.

Liquid-crystal based drift-free polarization modulators: Part I. Design and operation.

We report a new design for temperature-stable polarization modulators. Each modulator is composed of two liquid crystal variable retarders (LCVRs) positioned in such a way that their temperature drifts mutually compensate. We propose a model for the temperature-dependent polarization response of LCVRs, which permits us to establish expressions for the operating point of the system and for its accessible retardance range. We have validated such a model experimentally by thorough analyses of LCVR temperature responses, and we have built a polarization modulator that is stable over a wide range of temperature with commercially available LCVRs.

Monitoring subcutaneous tumors using Mueller polarimetry: study on two types of tumors.

A better understanding of tumor development is crucial for treating cancer. Polarimetric imaging is an interesting alternative for monitoring subcutaneous tumors as it is non-invasive. In this study, a Mueller spectro-polarimeter is used to monitor tumor development on mice injected with non-pigmented breast cancer cells or with pigmented murine melanoma cells. Three stages of non-pigmented tumor development are revealed with three polarimetric parameters. These stages also appear for pigmented tumors, although less clearly. A halo of high depolarization surrounding the non-pigmented tumor in the first stage allows the outlining of the tumor. Considering polarimetric parameters, a biological interpretation is proposed.

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN).

Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future-based on the Internet of (Medical) Things (IoT, IoMT)-accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry.

A covariate-constraint method to map brain feature space into lower dimensional manifolds.

Human brain connectome studies aim to both explore healthy brains, and extract and analyze relevant features associated with pathologies of interest. Usually this consists of modeling the brain connectome as a graph and using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension, low-sample-size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator an understanding of the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold learning methodology; the originality is that the investigator chooses one (or several) reduced variables. The proposed method is illustrated in two studies. The first one addresses comatose patients; the second one compares young and elderly populations. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of these differences.

Revisiting the generalized polar decomposition of Mueller matrices.

Mueller polarimetry is a powerful imaging modality that has been successfully applied to various application fields. Decomposition of Mueller matrices in elementary components is classically considered in order to unfold complex physical phenomena taking place in probed samples or scenes. In this context, the generalized polar decomposition, also known as Lu and Chipman decomposition, plays a prominent role. In this paper, we show that the set of candidate generalized polar decompositions is richer than the set used so far. Negative-determinant Mueller matrices are naturally addressed in the proposed framework. We show that taking into account those supplementary polar decompositions addresses issues raised in the literature. Application is carried out on synthetic and on measured Mueller matrices.

Mueller polarimetric imaging of biological tissues: classification in a decision-theoretic framework.

Mueller polarimetry is increasingly recognized as a powerful modality in biomedical imaging. Nevertheless, principled statistical analysis procedures are still lacking in this field. This paper presents a complete pipeline for polarimetric bioimages, with an application to ex vivo cervical precancer detection. In the preprocessing stage, we evaluate the replacement of pixels by superpixels. In the analysis stage, we resort to decision theory to select and tune a classifier. Performances of the retained classifier are evaluated. Decision theory provides a rigorous and versatile framework, allowing generalization to other pathologies, to other imaging procedures, and to classification problems involving more than two classes.

Ultrasonographic imaging of abomasal milk clotting and abomasal diameter in healthy and diarrheic calves.

In case of diarrhea calves are treated with oral rehydration solutions (ORS), which are known to increase abomasal pH and inhibit milk clotting in vitro. Nevertheless, recent studies have shown that ORS with HCO3(-) ≤ 62 mmol/L do not interfere with abomasal milk clotting in healthy calves. However, in diarrheic calves, feeding ORS and milk simultaneously may disturb abomasal curd formation and exacerbate diarrhea due to faster abomasal passage of ingesta. Therefore, the aim of the present study was to ultrasonographically examine abomasal milk clotting and diameter after feeding milk and milk replacer (MR) with and without ORS to healthy and diarrheic calves. Abomasal curd formation and diameter in healthy and diarrheic calves were ultrasonographically imaged before and after feeding milk, MR and ORS prepared in milk or MR. Feeding mixtures of milk or MR with ORS did not cause any remarkable differences in the ultrasonographic images of abomasal content. Moreover, abomasal milk clotting was not disturbed due to diarrhea. Statistically significant differences of abomasal diameter after feeding between healthy and diarrheic calves indicated that abomasal emptying is delayed in diarrheic calves. Hence, further studies are needed to determine reasons for decelerated abomasal passage in calves suffering from diarrhea.

The influence of alcoholic intoxication on the short-time energy function of speech.

This study investigates rhythmic features based on the short-time energy function of speech signals with the aim of finding robust, speaker-independent features that indicate speaker intoxication. Data from the German Alcohol Language Corpus, which comprises read, spontaneous, and command&control speech uttered by 162 speakers of both genders and various age groups when sober and intoxicated, were analyzed. Energy contours are compared directly (Root Mean Squared Error, statistical correlation, or the Euclidean distance in the spectral space of the contour) and by parameterization of the contour using the Discrete Cosine Transform (DCT) and the first and second moments of the lower DCT spectrum. Contours are also analyzed by Principal Components Analysis aiming at fundamental "eigen contour" changes that might encode intoxication. Energy contours differ significantly with intoxication in terms of distance measures, the second and fourth DCT coefficients, and the first and second moments of the lower DCT spectrum. Principal Components Analysis did not yield interpretable "eigen contours" that could be used in distinguishing intoxicated from sober contours.

Estimation of Mueller matrices using non-local means filtering.

This article addresses the estimation of polarization signatures in the Mueller imaging framework by non-local means filtering. This is an extension of previous work dealing with Stokes signatures. The extension is not straightforward because of the gap in complexity between the Mueller framework and the Stokes framework. The estimation procedure relies on the Cholesky decomposition of the coherency matrix, thereby ensuring the physical admissibility of the estimate. We propose an original parameterization of the boundary of the set of Mueller matrices, which makes our approach possible. The proposed method is fully unsupervised. It allows noise removal and the preservation of edges. Applications to synthetic as well as real data are presented.

Joint filtering estimation of Stokes vector images based on a nonlocal means approach.

Conventional estimation techniques of Stokes images from observed radiance images through different polarization filters suffer from noise contamination that hampers correct interpretation or even leads to unphysical estimated signatures. This paper presents an efficient restoration technique based on nonlocal means, permitting accurate estimation of smoothly variable polarization signatures in the Stokes image while preserving sharp transitions. The method is assessed on simulated data as well as on real images.

Hubs of brain functional networks are radically reorganized in comatose patients.

Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient "hub" nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks.

The influence of alcoholic intoxication on the fundamental frequency of female and male speakers.

This study investigates long-term features and utterance contours of fundamental frequency (f0) derived from the German Alcohol Language Corpus. The corpus comprises read, spontaneous, and command&control speech uttered by 148 speakers of both genders and various age groups when sober and intoxicated. f0 median, f0 range, and f0 contours are analyzed for intoxication and interactions with gender and age. Contours are compared both directly (root mean squared error, statistical correlation, or the Euclidean distance in the spectral space of the contour) and by parameterization of the contour using discrete cosine transform and the first and second moment of the lower contour spectrum. Results partly confirm earlier findings, i.e., f0 average and range are mostly raised with intoxication, and also suggest that the majority of speakers do not follow a general trend, but show idiosyncratic alterations to f0. f0 contours differ significantly with intoxication, but a more detailed analysis could not assign these changes to specific general form changes like decline or curvature. The results suggest that it is not possible to predict intoxication from f0 in a single model across different speakers. Instead a speaker-dependent model to account for the individual speaker behavior is proposed.

Computation of axonal elongation in head trauma finite element simulation.

In the case of head trauma, elongation of axons is thought to result in brain damage and to lead to Diffuse Axonal Injuries (DAI). Mechanical parameters have been previously proposed as DAI metric. Typically, brain injury parameters are expressed in terms of pressure, shearing stresses or invariants of the strain tensor. Addressing axonal deformation within the brain during head impact can improve our understanding of DAI mechanisms. A new technique based on directional measurements of water diffusion in soft tissue using Magnetic Resonance Imaging (MRI), called Diffusion Tensor Imaging (DTI), provides information on axonal orientation within the brain. The present study aims at coupling axonal orientation from a 12-patient-based DTI 3D picture, called "DTI atlas", with the Strasbourg University Finite Element Head Model (SUFEHM). This information is then integrated in head trauma simulation by computing axonal elongation for each finite element of the brain model in a post-processing of classical simulation results. Axonal elongation was selected as computation endpoint for its strong potential as a parameter for DAI prediction and location. After detailing the coupling technique between DTI atlas and the head FE model, two head trauma cases presenting different DAI injury levels are reconstructed and analyzed with the developed methodology as an illustration of axonal elongation computation. Results show that anisotropic brain structures can be realistically implemented into an existing finite element model of the brain. The feasibility of integrating axon fiber direction information within a dedicated post-processor is also established in the context of the computation of axonal elongation. The accuracy obtained when estimating level and location of the computed axonal elongation indicates that coupling classical isotropic finite element simulation with axonal structural anisotropy is an efficient strategy. Using this method, tensile elongation of the axons can be directly invoked as a mechanism for Diffuse Axonal Injury.

Recovery of polarimetric Stokes images by spatial mixture models.

A Bayesian approach for joint restoration and segmentation of polarization encoded images is presented with emphasis on both physical admissibility and smoothness of the solution. Two distinct models for the sought polarized radiances are used: (i) the polarized light at each site of the image is described by its Stokes vector, which directly follows a mixture of truncated Gaussians, explicitly assigning zero probability to inadmissible configurations and (ii) polarization at each site is represented by the coherency matrix, which is parameterized by a set of variables assumed to be generated by a spatially varying mixture of Gaussians. Application on real and synthetic images using the proposed methods assesses the pertinence of the approach.

Digestive activity evaluation by multichannel abdominal sounds analysis.

This paper introduces a complete methodology for abdominal sounds analysis, from signal acquisition to statistical data analysis. The goal is to evaluate if and how phonoenterograms can be used to detect different functioning modes of the normal gastrointestinal tract, both in terms of localization and of time evolution during the digestion. After the description of the acquisition protocol and the employed instrumentation, several signal processing steps are presented: wavelet denoising and segmentation, artifact suppression, and source localization. Next, several physiological features are extracted from the processed signals issued from a database of 14 healthy volunteers, recorded during 3 h after a standardized meal. Data analysis is performed using a multifactorial statistical method. Based on the introduced approach, we show that the abdominal regions of healthy volunteers present statistically significant phonoenterographic characteristics, which evolve differently during the normal digestion. The most significant feature allowing us to distinguish regions and time differences is the number of recorded sounds, but important information is also carried by sound amplitudes, frequencies, and durations. Depending on the considered feature, the sounds produced by different abdominal regions (especially stomach, ileocaecal, and lower abdomen regions) present a specific distribution over space and time. This information, statistically validated, is usable in further studies as a comparison term with other normal or pathological conditions.

Symmetric nonrigid image registration: application to average brain templates construction.

Image registration aims at estimating a consistent mapping between two images. Common techniques consist in choosing arbitrarily one image as a reference image and the other one as a floating image, thus leading to the estimation of inconsistent mappings. We present a symmetric formulation of the registration problem that maps the two images in a common coordinate system halfway between them. This framework has been considered to devise an efficient strategy for mapping a large set of images in a common coordinate system. Some results are presented in the context of 3-D nonrigid brain MR image registration for the construction of average brain templates.

Accurate inversion of 3-D transformation fields.

This correspondence addresses the inversion of 3-D transformation fields, which is a problem that typically arises in image warping problems. A topology preserving parametric B-spline-based representation of the deformation field is considered. Topology preservation ensures that the transformation is a one-to-one mapping and consequently that it is invertible. Inverting such transformation fields amounts to solving a system of nonlinear equations. To tackle this problem, we rely on interval analysis techniques. The proposed algorithm yields a solution whose accuracy is user-controlled. This method may be extended to any dense transformation field and also to deformations defined on a grid of points, by considering a projection in the space of topology preserving B-spline-based deformation fields. The performance of the algorithm is illustrated on transformation fields coming from intersubject brain registration.

A Bayesian approach for polarimetric data reduction: the Mueller imaging case.

In this paper, we extend to the Mueller imaging framework a formerly introduced Bayesian approach dealing with polarimetric data reduction and robust clustering of polarization encoded images in the piecewise constant case. The extension was made possible thanks to a suitable writing of the observation model in the Mueller context that relies on the system's coherency matrix and Cholesky decomposition such that the admissibility constraints are easily captured. This generalization comes at the cost of nonlinearity with respect to the parameters that have to be estimated. This estimation-clustering problem is tackled in a Bayesian framework where a hierarchical stochastic model based on a Markov random field proposed by Potts is used. This fully unsupervised approach is extensively tested over synthetic data as well as real Mueller images.

Polarimetric data reduction: a Bayesian approach.

In this paper, we introduce a general Bayesian approach to estimate polarization parameters in the Stokes imaging framework. We demonstrate that this new approach yields a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides a robust clustering of Stokes images in regard to image noises. The proposed approach is extensively evaluated by using synthetic simulated data and applied to cluster and retrieves the Stokes image issuing from a set of real measurements.