Shoulder pathology: estimating dominant upper-limb activity using 3D kinematic sensors.

Introduction. Quantification of daily upper-limb activity is a key determinant in evaluation of shoulder surgery. For a number of shoulder diseases, problem in performing daily activities have been expressed in terms of upper-limb usage and non-usage. Many instruments measure upper-limb movement but do not focus on the differentiations between the use of left or right shoulder. Several methods have been used to measure it using only accelerometers, pressure sensors or video-based analysis. However, there is no standard or widely used objective measure for upper-limb movement. We report here on an objective method to measure the movement of upper-limb and we examined the use of 3D accelerometers and 3D gyroscopes for that purpose. Methods. We studied 8 subjects with unilateral pathological shoulder (8 rotator cuff disease: 53 years old ± 8) and compared them to 18 control subjects (10 right handed, 8 left handed: 32 years old ± 8, younger than the patient group to be almost sure they don_t have any unrecognized shoulder pathology). The Simple Shoulder Test (SST) and Disabilities of the Arm and Shoulder Score (DASH) questionnaires were completed by each subject. Two modules with 3 miniature capacitive gyroscopes and 3 miniature accelerometers were fixed by a patch on the dorsal side of the distal humerus, and one module with 3 gyroscopes and 3 accelerometers were fixed on the thorax. The subject wore the system during one day (8 hours), at home or wherever he/she went. We used a technique based on the 3D acceleration and the 3D angular velocities from the modules attached on the humerus. Results. As expected, we observed that for the stand and sit postures the right side is more used than the left side for a healthy right-handed person(idem on the left side for a healthy left-handed person). Subjects used their dominant upper-limb 18% more than the non-dominant upper-limb. The measurements on patients in daily life have shown that the patient has used more his non affected and non dominant side during daily activity if the dominant side = affected shoulder. If the dominant side affected shoulder, the difference can be showed only during walking period. Discussion-Conclusion. The technique developed and used allowed the quantification of the difference between dominant and non dominant side, affected and unaffected upper-limb activity. These results were encouraging for future evaluation of patients with shoulder injuries, before and after surgery. The feasibility and patient acceptability of the method using body fixed sensors for ambulatory evaluation of upper limbs kinematics was shown.

Estimating traveltimes and groundwater flow patterns using 3D time-lapse crosshole ERT imaging of electrical resistivity fluctuations induced by infiltrating river water

The infiltration of river water into aquifers is of high relevance to drinking-water production and is a key driver of biogeochemical processes in the hyporheic and riparian zone, but the distribution and quantification of the infiltrating water are difficult to determine using conventional hydrological methods (e.g., borehole logging and tracer tests). By time-lapse inverting crosshole ERT (electrical resistivity tomography) monitoring data, we imaged groundwater flow patterns driven by river water infiltrating a perialpine gravel aquifer in northeastern Switzerland. This was possible because the electrical resistivity of the infiltrating water changed during rainfall-runoff events. Our time-lapse resistivity models indicated rather complex flow patterns as a result of spatially heterogeneous bank filtration and aquifer heterogeneity. The upper part of the aquifer was most affected by the river infiltrate, and the highest groundwater velocities and possible preferential flow occurred at shallow to intermediate depths. Time series of the reconstructed resistivity models matched groundwater electrical resistivity data recorded on borehole loggers in the upper and middle parts of the aquifer, whereas the resistivity models displayed smaller variations and delayed responses with respect to the logging data. in the lower part. This study demonstrated that crosshole ERT monitoring of natural electrical resistivity variations of river infiltrate could be used to image and quantify 3D bank filtration and aquifer dynamics at a high spatial resolution.

Comparison of 3D segmented gradient-echo and steady-state free precession coronary MRI sequences in patients with coronary artery disease.

OBJECTIVE: Our objective was to compare two state-of-the-art coronary MRI (CMRI) sequences with regard to image quality and diagnostic accuracy for the detection of coronary artery disease (CAD). SUBJECTS AND METHODS: Twenty patients with known CAD were examined with a navigator-gated and corrected free-breathing 3D segmented gradient-echo (turbo field-echo) CMRI sequence and a steady-state free precession sequence (balanced turbo field-echo). CMRI was performed in a transverse plane for the left coronary artery and a double-oblique plane for the right coronary artery system. Subjective image quality (1- to 4-point scale, with 1 indicating excellent quality) and objective image quality parameters were independently determined for both sequences. Sensitivity, specificity, and accuracy for the detection of significant (> or = 50% diameter) coronary artery stenoses were determined as defined in invasive catheter X-ray coronary angiography. RESULTS: Subjective image quality was superior for the balanced turbo field-echo approach (1.8 +/- 0.9 vs 2.3 +/- 1.0 for turbo field-echo; p < 0.001). Vessel sharpness, signal-to-noise ratio, and contrast-to-noise ratio were all superior for the balanced turbo field-echo approach (p < 0.01 for signal-to-noise ratio and contrast-to-noise ratio). Of the 103 segments, 18% of turbo field-echo segments and 9% of balanced turbo field-echo segments had to be excluded from disease evaluation because of insufficient image quality. Sensitivity, specificity, and accuracy for the detection of significant coronary artery stenoses in the evaluated segments were 92%, 67%, 85%, respectively, for turbo field-echo and 82%, 82%, 81%, respectively, for balanced turbo field-echo. CONCLUSION: Balanced turbo field-echo offers improved image quality with significantly fewer nondiagnostic segments when compared with turbo field-echo. For the detection of CAD, both sequences showed comparable accuracy for the visualized segments.

Mapping the human connectome at multiple scales with diffusion spectrum MRI.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction.

Respiratory motion is a major source of artifacts in cardiac magnetic resonance imaging (MRI). Free-breathing techniques with pencil-beam navigators efficiently suppress respiratory motion and minimize the need for patient cooperation. However, the correlation between the measured navigator position and the actual position of the heart may be adversely affected by hysteretic effects, navigator position, and temporal delays between the navigators and the image acquisition. In addition, irregular breathing patterns during navigator-gated scanning may result in low scan efficiency and prolonged scan time. The purpose of this study was to develop and implement a self-navigated, free-breathing, whole-heart 3D coronary MRI technique that would overcome these shortcomings and improve the ease-of-use of coronary MRI. A signal synchronous with respiration was extracted directly from the echoes acquired for imaging, and the motion information was used for retrospective, rigid-body, through-plane motion correction. The images obtained from the self-navigated reconstruction were compared with the results from conventional, prospective, pencil-beam navigator tracking. Image quality was improved in phantom studies using self-navigation, while equivalent results were obtained with both techniques in preliminary in vivo studies.

Automated Lesion Detection-Based Symptome Mapping In Patients With Sensory Deficits

The human brain is the most complex structure known. With its high number of cells, number of connections and number of pathways it is the source of every thought in the world. It consumes 25% of our oxygen and suffers very fast from a disruption of its supply. An acute event, like a stroke, results in rapid dysfunction referable to the affected area. A few minutes without oxygen and neuronal cells die and subsequently degenerate. Changes in the brains incoming blood flow alternate the anatomy and physiology of the brain. All stroke events leave behind a brain tissue lesion. To rapidly react and improve the prediction of outcome in stroke patients, accurate lesion detection and reliable lesion-based function correlation would be very helpful. With a number of neuroimaging and clinical data of cerebral injured patients this study aims to investigate correlations of structural lesion locations with sensory functions.

Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence.

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images.

The goal of this study was to investigate the impact of computing parameters and the location of volumes of interest (VOI) on the calculation of 3D noise power spectrum (NPS) in order to determine an optimal set of computing parameters and propose a robust method for evaluating the noise properties of imaging systems. Noise stationarity in noise volumes acquired with a water phantom on a 128-MDCT and a 320-MDCT scanner were analyzed in the spatial domain in order to define locally stationary VOIs. The influence of the computing parameters in the 3D NPS measurement: the sampling distances bx,y,z and the VOI lengths Lx,y,z, the number of VOIs NVOI and the structured noise were investigated to minimize measurement errors. The effect of the VOI locations on the NPS was also investigated. Results showed that the noise (standard deviation) varies more in the r-direction (phantom radius) than z-direction plane. A 25 × 25 × 40 mm(3) VOI associated with DFOV = 200 mm (Lx,y,z = 64, bx,y = 0.391 mm with 512 × 512 matrix) and a first-order detrending method to reduce structured noise led to an accurate NPS estimation. NPS estimated from off centered small VOIs had a directional dependency contrary to NPS obtained from large VOIs located in the center of the volume or from small VOIs located on a concentric circle. This showed that the VOI size and location play a major role in the determination of NPS when images are not stationary. This study emphasizes the need for consistent measurement methods to assess and compare image quality in CT.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

3D culture of postnatal retinal stem cells using self assembling polymers

PURPOSE We have previously shown that retinal stem cells (RSCs) can be isolated from the radial glia population of the newborn mouse retina (Angénieux et al., 2006). These RSCs have a great capacity to renew and to generate a large number of neurons including cells differentiated towards the photoreceptor lineage (Mehri-Soussi et al., 2006). However, recent published results from our lab revealed that such cells have a poor integration and survival rate after grafting. The uncontrolled environment of a retina seems to prevent good integration and survival after grafting in vivo. To bypass this problem, we are evaluating the possibility of generating in vitro a hemi-retinal tissue before transplantation. METHODS RSC were expanded and cells passaged <10 were seeded in a solution containing poly-ethylene-glycol (PEG) polymer based hydrogels crosslinked with peptides that are chosen to be substrates for matrix metalloproteinases. Various doses of cross linkers peptides allowing connections between PEG polymers were tested. Different growth factors were studied to stimulate cell proliferation and differentiation. RESULTS Cells survived only in the presence of EGF and FGF-2 and generated colonies with a sphere shape. No cells migrated within the gel. To improve the migration and the repartition of the cells in the gels, the integrin ligand RGDSP was added into the gel. In the presence of FGF-2 and EGF, newly formed cell clusters appeared by cell proliferation within several days, but again no outspreading of cells was observed. No difference was even seen when the stiffness of the hydrogels or the concentration of the integrin ligand RGDSP were changed. However, our preliminary results show that RSCs still form spheres when laminin is entrapped in the gel, but they started to spread out having a neuronal morphology after around 2 weeks. The neuronal population was assessed by the presence of the neuronal marker b-tubulin-III. This differentiation was achieved after successive steps of stimulations including FGF-2 and EGF, and then only FGF-2. Glial cells were also present. Further characterizations are under process. CONCLUSIONS RSC can be grown in 3D. Preliminary results show that neuronal cell phenotype acquisition can be instructed by exogenous stimulations and factors linked to the gel. Further developments are necessary to form a homogenous tissue containing retinal cells.

MRS glucose mapping and PET joining forces: re-evaluation of the lumped constant in the rat brain under isoflurane anaesthesia.

Although numerous positron emission tomography (PET) studies with (18) F-fluoro-deoxyglucose (FDG) have reported quantitative results on cerebral glucose kinetics and consumption, there is a large variation between the absolute values found in the literature. One of the underlying causes is the inconsistent use of the lumped constants (LCs), the derivation of which is often based on multiple assumptions that render absolute numbers imprecise and errors hard to quantify. We combined a kinetic FDG-PET study with magnetic resonance spectroscopic imaging (MRSI) of glucose dynamics in Sprague-Dawley rats to obtain a more comprehensive view of brain glucose kinetics and determine a reliable value for the LC under isoflurane anaesthesia. Maps of Tmax /CMRglc derived from MRSI data and Tmax determined from PET kinetic modelling allowed to obtain an LC-independent CMRglc . The LC was estimated to range from 0.33 ± 0.07 in retrosplenial cortex to 0.44 ± 0.05 in hippocampus, yielding CMRglc between 62 ± 14 and 54 ± 11 μmol/min/100 g, respectively. These newly determined LCs for four distinct areas in the rat brain under isoflurane anaesthesia provide means of comparing the growing amount of FDG-PET data available from translational studies.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.