Siemens (Germany)

Measurements of the Hall voltage of a two-dimensional electron gas, realized with a silicon metal-oxide-semiconductor field-effect transistor, show that the Hall resistance at particular, experimentally well-defined surface carrier concentrations has fixed values which depend only on the fine-structure constant and speed of light, and is insensitive to the geometry of the device. Preliminary data are reported.

In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.

At room temperature a large magnetoresistance, \ensuremath{\Delta}R/R(H=0), of 60% has been observed in thin magnetic films of perovskitelike La-Ba-Mn-O. The films were grown epitaxially on ${\mathrm{SrTiO}}_{3}$ substrates by off-axis laser deposition. In the as-deposited state, the Curie temperature and the saturation magnetization were considerably lower compared to bulk samples, but were increased by a subsequent heat treatment. The samples show a drop in the resistivity at the magnetic transition, and the existence of magnetic polarons seems to dominate the electric transport in this region.

BACKGROUND: Recent studies indicate that magnetic resonance imaging (MRI) after the administration of contrast material can be used to distinguish between reversible and irreversible myocardial ischemic injury regardless of the extent of wall motion or the age of the infarct. We hypothesized that the results of contrast-enhanced MRI can be used to predict whether regions of abnormal ventricular contraction will improve after revascularization in patients with coronary artery disease. METHODS: Gadolinium-enhanced MRI was performed in 50 patients with ventricular dysfunction before they underwent surgical or percutaneous revascularization. The transmural extent of hyperenhanced regions was postulated to represent the transmural extent of nonviable myocardium. The extent of regional contractility at the same locations was determined by cine MRI before and after revascularization in 41 patients. RESULTS: Contrast-enhanced MRI showed hyperenhancement of myocardial tissue in 40 of 50 patients before revascularization. In all patients with hyperenhancement the difference in image intensity between hyperenhanced regions and regions without hyperenhancement was more than 6 SD. Before revascularization, 804 of the 2093 myocardial segments analyzed (38 percent) had abnormal contractility, and 694 segments (33 percent) had some areas of hyperenhancement. In an analysis of all 804 dysfunctional segments, the likelihood of improvement in regional contractility after revascularization decreased progressively as the transmural extent of hyperenhancement before revascularization increased (P<0.001). For instance, contractility increased in 256 of 329 segments (78 percent) with no hyperenhancement before revascularization, but in only 1 of 58 segments with hyperenhancement of more than 75 percent of tissue. The percentage of the left ventricle that was both dysfunctional and not hyperenhanced before revascularization was strongly related to the degree of improvement in the global mean wall-motion score (P<0.001) and the ejection fraction (P<0.001) after revascularization. CONCLUSIONS: Reversible myocardial dysfunction can be identified by contrast-enhanced MRI before coronary revascularization.

Patterns. Architectural Patterns. Design Patterns. Idioms. Pattern Systems. Patterns and Software Architecture. The Pattern Community. Where Will Patterns Go? Notations. Glossary. References. Index of Patterns.

A magnetic resonance imaging method is presented for quantifying the degree to which water diffusion in biologic tissues is non-Gaussian. Since tissue structure is responsible for the deviation of water diffusion from the Gaussian behavior typically observed in homogeneous solutions, this method provides a specific measure of tissue structure, such as cellular compartments and membranes. The method is an extension of conventional diffusion-weighted imaging that requires the use of somewhat higher b values and a modified image postprocessing procedure. In addition to the diffusion coefficient, the method provides an estimate for the excess kurtosis of the diffusion displacement probability distribution, which is a dimensionless metric of the departure from a Gaussian form. From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues. Diffusional kurtosis imaging is related to q-space imaging methods, but is less demanding in terms of imaging time, hardware requirements, and postprocessing effort. It may be useful for assessing tissue structure abnormalities associated with a variety of neuropathologies.

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.

In this paper, we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure. Our variational formulation consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image features, such as object boundaries. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed variational level set formulation has three main advantages over the traditional level set formulations. First, a significantly larger time step can be used for numerically solving the evolution partial differential equation, and therefore speeds up the curve evolution. Second, the level set function can be initialized with general functions that are more efficient to construct and easier to use in practice than the widely used signed distance function. Third, the level set evolution in our formulation can be easily implemented by simple finite difference scheme and is computationally more efficient. The proposed algorithm has been applied to both simulated and real images with promising results.

In recent years, there has been an upsurge of interest in multihop-augmented infrastructure-based networks in both the industry and academia, such as the seed concept in 3GPP, mesh networks in IEEE 802.16, and converge extension of HiperLAN/2 through relays or user-cooperative diversity mesh networks. This article, a synopsis of numerous contributions to the working group 4 of the wireless world research forum and other research work, presents an overview of important topics and applications in the context of relaying. It covers different approaches to exploiting the benefits of multihop communications via relays, such as solutions for radio range extension in mobile and wireless broadband cellular networks (trading range for capacity), and solutions to combat shadowing at high radio frequencies. Furthermore, relaying is presented as a means to reduce infrastructure deployment costs. It is also shown that through the exploitation of spatial diversity, multihop relaying can enhance capacity in cellular networks. We wish to emphasize that while this article focuses on fixed relays, many of the concepts presented can also be applied to systems with moving relays.

RATIONALE AND OBJECTIVES: To characterize and compare commercially available contrast media (CM) for magnetic resonance imaging (MRI) in terms of their relaxivity at magnetic field strengths ranging from 0.47 T to 4.7 T at physiological temperatures in water and in plasma. Relaxivities also were quantified in whole blood at 1.5 T. METHODS: Relaxivities of MRI-CM were determined by nuclear magnetic resonance (NMR) spectroscopy at 0.47 T and MRI phantom measurements at 1.5 T, 3 T, and 4.7 T, respectively. Both longitudinal (T1) and transverse relaxation times (T2) were measured by appropriate spin-echo sequences. Nuclear magnetic resonance dispersion (NMRD) profiles were also determined for all agents in water and in plasma. RESULTS: Significant dependencies of relaxivities on the field strength and solvents were quantified. Protein binding leads to both increased field strength and solvent dependencies and hence to significantly altered T1 relaxivity values at higher magnetic field strengths. CONCLUSIONS: Awareness of the field strength and solvent associated with relaxivity data is crucial for the comparison and evaluation of relaxivity values. Data observed at 0.47 T can thus be misleading and should be replaced by relaxivities measured at 1.5 T and at 3 T in plasma at physiological temperature.

Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute. 1.

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.

Industrie 4.0 - the “brand” name of the German initiative driving the future of manufacturing - is one of several initiatives around the globe emphasizing the importance of industrial manufacturing for economy and society. Besides the socio-economical if not political question which has to be answered - including the question about the future of labor - there are a couple of substantial technical and technological questions that have to be taken care of as well.

PURPOSE: To design a segmented inversion-recovery turbo fast low-angle shot (turboFLASH) magnetic resonance (MR) imaging pulse sequence for the visualization of myocardial infarction, compare this technique with other MR imaging approaches in a canine model of ischemic injury, and evaluate its utility in patients with coronary artery disease. MATERIALS AND METHODS: Six dogs and 18 patients were examined. In dogs, infarction was produced and images were acquired by using 10 different pulse sequences. In patients, the segmented turboFLASH technique was used to acquire contrast material-enhanced images 19 days +/- 7 (SD) after myocardial infarction. RESULTS: Myocardial regions of increased signal intensity were observed in all animals and patients at imaging. With the postcontrast segmented turboFLASH sequence, the signal intensity of the infarcted myocardium was 1,080% +/- 214 higher than that of the normal myocardium in dogs-nearly twice that of the next best sequence tested and approximately 10-fold greater than that in previous reports. All 18 patients with myocardial infarction demonstrated high signal intensity at imaging. On average, the signal intensity of the high-signal-intensity regions in patients was 485% +/- 43 higher than that of the normal myocardium. CONCLUSION: The segmented inversion-recovery turboFLASH sequence produced the greatest differences in regional myocardial signal intensity in animals. Application of this technique in patients with infarction substantially improved differentiation between injured and normal regions.

Image distortion due to field gradient eddy currents can create image artifacts in diffusion-weighted MR images. These images, acquired by measuring the attenuation of NMR signal due to directionally dependent diffusion, have recently been shown to be useful in the diagnosis and assessment of acute stroke and in mapping of tissue structure. This work presents an improvement on the spin-echo (SE) diffusion sequence that displays less distortion and consequently improves image quality. Adding a second refocusing pulse provides better image quality with less distortion at no cost in scanning efficiency or effectiveness, and allows more flexible diffusion gradient timing. By adjusting the timing of the diffusion gradients, eddy currents with a single exponential decay constant can be nulled, and eddy currents with similar decay constants can be greatly reduced. This new sequence is demonstrated in phantom measurements and in diffusion anisotropy images of normal human brain.

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table. We use the resulting dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network (GQ-CNN) model that rapidly predicts the probability of success of grasps from depth images, where grasps are specified as the planar position, angle, and depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8s with a success rate of 93% on eight known objects with adversarial geometry and is 3 faster than registering point clouds to a precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp planner also has the highest success rate on a dataset of 10 novel rigid objects and achieves 99% precision (one false positive out of 69 grasps classified as robust) on a dataset of 40 novel household objects, some of which are articulated or deformable. Code, datasets, videos, and supplementary material are available at http://berkeleyautomation.github.io/dex-net.

Wheeled vehicles are often incapable of transporting heavy materials over rough terrain or up staircases. Lower extremity exoskeletons supplement human intelligence with the strength and endurance of a pair of wearable robotic legs that support a payload. This paper summarizes the design and analysis of the Berkeley lower extremity exoskeleton (BLEEX). The anthropomorphically based BLEEX has 7 DOF per leg, four of which are powered by linear hydraulic actuators. The selection of the DOF, critical hardware design aspects, and initial performance measurements of BLEEX are discussed.

BACKGROUND: Heart failure treatment depends partly on the underlying cause of the disease. We evaluated cardiovascular magnetic resonance (CMR) for the problem of differentiating dilated cardiomyopathy (DCM) from left ventricular (LV) dysfunction caused by coronary artery disease (CAD). METHODS AND RESULTS: Late gadolinium enhancement with CMR was performed in 90 patients with heart failure and LV systolic dysfunction (63 patients with DCM and unobstructed coronary arteries and 27 with significant CAD at angiography). We also studied 15 control subjects with no coronary risk factors and/or unobstructed coronary arteries. None (0%) of the control subjects had myocardial gadolinium enhancement; however, all patients (100%) with LV dysfunction and CAD had enhancement, which was subendocardial or transmural. In patients with DCM, there were 3 findings: no enhancement (59%); myocardial enhancement indistinguishable from the patients with CAD (13%); and patchy or longitudinal striae of midwall enhancement clearly different from the distribution in patients with CAD (28%). CONCLUSIONS: Gadolinium CMR is a powerful technique to distinguish DCM from LV dysfunction related to CAD and yields new insights in DCM. These data suggest that using the coronary angiogram as the arbiter for the presence of LV dysfunction caused by CAD could have lead to an incorrect assignment of DCM cause in 13% of patients, possibly because of coronary recanalization after infarction. The midwall myocardial enhancement in patients with DCM is similar to the fibrosis found at autopsy; it has not previously been visualized in vivo and warrants further investigation. CMR may become a useful alternative to routine coronary angiography in the diagnostic workup of DCM.