Siemens

About: Siemens is a company organization based out in Munich, Germany. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 106091 authors who have published 169096 publications receiving 1511029 citations. The organization is also known as: Siemens bestaat in Nederland sinds 1879 & Siemens Aktiengesellschaft.

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

New Method for High-Accuracy Determination of the Fine-Structure Constant Based on Quantized Hall Resistance

Abstract: 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.

Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Abstract: 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): MRI methods.

Abstract: Dementia, one of the most feared associates of increasing longevity, represents a pressing public health problem and major research priority. Alzheimer's disease (AD) is the most common form of dementia, affecting many millions around the world. There is currently no cure for AD, but large numbers of novel compounds are currently under development that have the potential to modify the course of the disease and slow its progression. There is a pressing need for imaging biomarkers to improve understanding of the disease and to assess the efficacy of these proposed treatments. Structural magnetic resonance imaging (MRI) has already been shown to be sensitive to presymptomatic disease (1-10) and has the potential to provide such a biomarker. For use in large-scale multicenter studies, however, standardized methods that produce stable results across scanners and over time are needed. The Alzheimer's Disease Neuroimaging Initiative (ADNI) study is a longitudinal multisite observational study of elderly individuals with normal cognition, mild cognitive impairment (MCI), or AD (11,12). It is jointly funded by the National Institutes of Health (NIH) and industry via the Foundation for the NIH. The study will assess how well information (alone or in combination) obtained from MRI, (18F)-fludeoyglucose positron emission tomography (FDG PET), urine, serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical and neuropsychometric assessments, can measure disease progression in the three groups of elderly subjects mentioned above. At the 55 participating sites in North America, imaging, clinical, and biologic samples will be collected at multiple time points in 200 elderly cognitively normal, 400 MCI, and 200 AD subjects. All subjects will be scanned with 1.5 T MRI at each time point, and half of these will also be scanned with FDG PET. Subjects not assigned to the PET arm of the study will be eligible for 3 T MRI scanning. The goal is to acquire both 1.5 T and 3 T MRI studies at multiple time points in 25% of the subjects who do not undergo PET scanning [R2C1]. CSF collection at both baseline and 12 months is targeted for 50% of the subjects. Sampling varies by clinical group. Healthy elderly controls will be sampled at 0, 6, 12, 24, and 36 months. Subjects with MCI will be sampled at 0, 6, 12, 18, 24, and 36 months. AD subjects will be sampled at 0, 6, 12, and 24 months. Major goals of the ADNI study are: to link all of these data at each time point and make this repository available to the general scientific community; to develop technical standards for imaging in longitudinal studies; to determine the optimum methods for acquiring and analyzing images; to validate imaging and biomarker data by correlating these with concurrent psychometric and clinical assessments; and to improve methods for clinical trials in MCI and AD. The ADNI study overall is divided into cores, with each core managing ADNI-related activities within its sphere of expertise: clinical, informatics, biostatistics, biomarkers, and imaging. The purpose of this report is to describe the MRI methods and decision-making process underlying the selection of the MRI protocol employed in the ADNI study.