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In this paper we present results of the simulation analysis of the impact of transmission parameters and distance between LoRa node and gateway on communication performance (packet loss rate and energy consumed by nodes). FLoRa simulation framework based on Omnet++ was used to vary spreading factor, bandwidth, coding rate and transmission power. For given distance of 100 m, Lora gateway successfully received the packets in 80 configurations. In particular case, when the distance between node and gateway was doubled, just 8 configurations were able to deliver the packets. Finally, we used 5 values of path loss variance in range from 0 to 7.08 dB with a distance of 400 m, and the success rate of data transmission from node to the gateway decreased to 48.83%.

In the course of the German energy transition, the energy system is facing major transformations. In order to analyze and understand structural changes, system modeling is an appropriate approach to create an image of reality. Various models of the electricity and the gas market allow for an independent analysis of both systems. Due to the increasing importance of sector coupling technologies, a separate analysis of the two markets may no longer be sufficient. This paper deals with the difficulty of connecting electricity market models to gas market models while keeping the complexity of the resulting optimization problem low. Coupling points of the two models are identified and analyzed. Furthermore, in the methodology part, an iterative link between the two models is developed. In exemplary studies, the interaction between the two models is investigated for a lignite exit scenario in Germany.

Traditional cloud-based infrastructures are not enough for the current demands of Internet of Things (IoT) applications. Two major issues are the limitations in terms of latency and network bandwidth. In recent years, the concepts of fog computing and edge computing were proposed to alleviate these limitations by moving data processing capabilities closer to the network edge. Considering IoT growth and development forecasts, we believe the full potential of IoT can, in many cases, only be unlocked by combining cloud, fog and edge computing. This paper discusses four possible approaches for distributing workload among these levels. We also highlight developments and possibilities as well as consider challenges for implementation in the areas of hardware, machine learning, security, privacy and communication.

Accurate liver segmentation from computed tomography (CT) images is problematic due to non-uniform density, weak boundaries and because there may be multiple liver tumors that have heterogeneous intensities in region(s) of interest (ROIs). So we propose a generalized energy framework that harnesses the statistical intensity approximation with image data on graphs. Our statistical energy term takes advantage of the mixture-of-mixtures Gaussian model to approximate the probability density distribution of the liver and background to better differentiate between the two. The probability density estimation can be combined with the spatial cohesion of the graph-based Dirichlet integral by using graph calculus. Matrix decomposition and differentiation are used to minimize our proposed energy functional. We tested our approach on 20 public high-contrast CT images with single and multiple liver tumors. Our method had an average dice similarity coefficient (DSC) of 93.75±1.29%, an average false positive (FP) rate of 9.43±3.52% and an average false negative (FN) rate of 3.48±1.48%. Our method outperformed the benchmark graph-based Random Walker algorithm (average DSC=81.97±4.09%, average FP rate 34.10±10.53%, and average FN rate 7.10±4.35%). ARC

Prostate cancer is the second most common cause of cancer deaths in males. Accurate prostate segmentation from magnetic resonance (MR) images is critical to the diagnosis and treatment of prostate cancer. Automated prostate segmentation is challenging due to the variety in shapes and sizes of the prostate. Furthermore, the expected boundaries of ROIs are often indistinct, while heterogeneity concurrently exists within the ROIs. To address these challenges, we propose an automated approach that incorporates the local intensity features by random walker (RW) algorithm and global probability knowledge from an atlas to better describe unique characteristics of the prostate in MR images. We formulated a new RW weight function to take into account atlas probabilities and intensity differences. The prior knowledge from the atlas probability map not only reflects the statistical shape approximation of the prostate but also provides confinement and guidance for RW segmentation. Our approach was validated and compared with the conventional RW algorithm on segmenting 30 3-T prostate MR volumes. The experimental results indicated that our approach with an average DSC of 80.7±5.1%, outperformed that of the conventional RW (average DSC = 71.9±9.1%) and several other reported methods in terms of DSC accuracy and robustness.

The human colon is an anaerobic environment densely populated with bacterial species creating an ecosystem imperative to physiological function with regards to metabolism of non-digestable residues. Using a mathematical representation of carbohydrate degradation as an experimental platform, the effect in variability of system flow rate and dietary fiber consumption to a measure of ecological diversity in the colon was studied. Results demonstrate a low variance in bacterial diversity, and strong linear relationships between variables. These relationships can provide good approximations of the intestinal microflora behaviour with respect to flow rate and fiber consumption in a simple computation despite the underlying mechanisms being described in a non-linear, highly complex system.

This paper describes how limited liability leads to risk-loving behaviour of nuclear power companies and on average too unsafe nuclear power plants. By reviewing current regulatory regimes, we show that this issue is not sufficiently taken care of by today's regulation. Therefore, we evaluate five regulatory instruments: (1) safety regulation, (2) minimum equity requirements, (3) mandatory insurance, (4) risk-sharing pools, and (5) catastrophe bonds. We conclude that none of these instruments in its pure form is recommendable. Thus, we propose a new approach that in its core consists of a two-stage procedure. On the first stage, capital markets assess the risk stemming from each nuclear power plant via catastrophe bonds. In the second step, the regulator uses this private risk assessment and intervenes by charging an actuarial fair premium in the sense of a Pigouvian risk fee. Society eventually acts as an explicit insurer for nuclear risk and is on average fairly compensated for the risk it is taking over.

Combined PET-CT is now increasingly used for the clinical evaluation of cancer and is arguably the best tool to stage non-small cell lung cancer (NSCLC). We propose a framework to better delineate lung tumors which utilizes information from PET and CT images. The framework is based on a downhill region growing technique for PET and a Gaussian mixture model for CT images. We applied our framework in 20 PET-CT studies from patients with NSCLC. Experiments show that our method is able to delineate lung tumors in complex cases where the tumors are located near other organs with similar intensities in PET images or when the tumors extends into the chest wall or the mediastinum. We also compared 10 of the datasets with experts performing manual delineation, which produced a volumetric overlapped fraction of 0.78 ± 0.10.

PET-CT provides aligned anatomical (CT) and functional (PET) images in a single scan, and has the potential to improve brain PET image segmentation, which can in turn improve quantitative clinical analyses. We propose a statistical segmentation algorithm that incorporates the prior anatomical knowledge represented by probabilistic brain atlas into the variational Bayes inference to delineate gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in brain PET-CT images. Our approach adds an additional novel aspect by allowing voxels to have variable and adaptive prior probabilities of belonging to each class. We compared our algorithm to the segmentation approaches implemented in the expectation maximization segmentation (EMS) and statistical parametric mapping (SPM8) packages in 26 clinical cases. The results show that our algorithm improves the accuracy of brain PET-CT image segmentation.

Positron emission tomography - computed tomography (PETCT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a content-based image retrieval system for retrieving similar cases from an imaging database as a reference dataset to aid the physicians in PET-CT scan interpretation. Problematic areas in diagnosis are the abnormal FDG uptake in the parenchymal lung tumor and in the regional nodes in the pulmonary hilar regions and the mediastinum. The primary tumor and the nodal disease are detected from the scans of thorax with learning-based techniques and a voting method for 3D object localization. Similar cases are then retrieved based on the similarity measure between the feature vectors of the cases. Our preliminary evaluation with clinical data from lung cancer patients suggests our approach is accurate with high retrieval precision.

Neuroimaging is a fundamental component of the neurological diagnosis. The greatly increased volume and complexity of neuroimaging datasets has created a need for efficient image management and retrieval. In this paper, we advance a content-based retrieval framework for 3D functional neuroimaging data based on 3D curvelet transforms. The localized volumetric texture feature was extracted by a 3D digital curvelet transform from parametric image of cerebral metabolic rate of glucose consumption with a set of adaptive disorder-oriented masks for each type of neurological disorder. The results, using 142 clinical dementia studies, show that our proposed approach supports efficient and high performance neuroimaging data retrieval.

Content-based image retrieval (CBIR) has been an active research area since mid 90’s with major focus on feature extraction, due to its significant impact on image retrieval performance. When applying CBIR in the medical domain, different imaging modalities and anatomical regions require different feature extraction methods that integrate some domain-specific knowledge for effective image retrieval. This paper presents some new CBIR techniques for positron emission tomography - computed tomography (PET-CT) lung images, which exhibit special characteristics such as similar image intensities of lung tumors and soft tissues. Adaptive texture feature extraction and structural signature representation are proposed, and implemented based on our recently developed CBIR framework. Evaluation of the method on clinical data from lung cancer patients with various disease stages demonstrates its benefits.

The volume and complexity of neurological images have significantly increased, which leads to challenges in efficient data management and retrieval. In this paper, we developed a new content-based image retrieval framework with the localized multiscale Discrete Curvelet Transform (DCvT) features extracted from parametric neurological images. We also compared the performance of three different irregular-to-regular shape padding methods. 142 patient data with neurodegenerative disorders were used in the evaluation. The preliminary results show that our proposed framework supports fast neuroimaging retrieval, and the orthographic projection method can reduce the computational complexity and has a great potential to improve the retrieval for indefinite cases.

This paper presents a framework for effective and fast content-based image retrieval for multi-modality PET-CT lung scans. PET-CT scans present significant advantages in tumor staging, but also place new challenges in computerized image analysis and retrieval. Our framework comprises 5 major components: lung field estimation, texture feature extraction, feature categorization, refinement using SVM, and similarity measure. Clinical data from lung cancer patients are used as case studies, and effective retrieval performance is demonstrated.

Functional neuroimaging has an important role in non-invasive diagnosis of neurodegenerative disorders. There are now large volumes of imaging data generated by functional imaging technologies and so there is a need to efficiently manage and retrieve these data. In this paper, we propose a new scheme for efficient 3D content-based neurological image retrieval. 3D pathology-centric masks were adaptively designed and applied for extracting CMRGlc (cerebral metabolic rate of glucose consumption) texture features with volumetric co-occurrence matrices from neurological FDG PET images. Our results, using 93 clinical dementia studies, show that our approach offers a robust and efficient retrieval mechanism for relevant clinical cases and provides advantages in image data analysis and management.

Automated liver segmentation is problematic due to variations in liver shape / size and because the liver has a similar density distribution to surrounding structures. We propose a method that: 1) utilizes iteratively constructed probabilistic liver and rib cage atlases, 2) conducts the Gaussian distribution analysis to avoid incorrectly classifying the irrelevant surrounding tissues as ‘liver region’ in the conventional probabilistic atlas based method, and maps the intensity range of the input candidate liver region onto the liver atlas, 3) retrieves the ‘missing parts’ of the liver by deformable registration. Our approach is automated and able to segment the liver from high-contrast and low-contrast CT volumes. Forty clinical CT studies were used for atlas construction and validation. Our method outperformed two other probabilistic atlas-based liver segmentation methods.

The increasing prevalence of dual medical imaging modalities, such as PET-CT scanners, poses both challenges and opportunities to image segmentation, as they provide distinct but complementary information. In this paper, we propose a novel segmentation algorithm for 3D brain PET-CT images, which classifies each voxel by fusing the voxel's memberships estimated from four points of view using the PET information, CT information, smoothness prior, and probabilistic brain atlas. All memberships having the same dynamic range greatly facilitates weighting the contribution of the four different information sources. The probabilistic brain atlas estimated for each PET-CT image from a set of training samples provides the anatomical information to the segmentation process. We compared the proposed algorithm to three single-classifier based methods, PET-based SPM algorithm, CT-based Otsu thresholding, and PET-CT based MAP-MRF algorithm. The experimental results in 11 clinical brain PET-CT studies demonstrate that the novel algorithm is capable of providing more accurate and reliable segmentation.

The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.

The use of functional (PET) information from PET-CT scanners to assist liver segmentation in CT data has yet to be addressed. In this work we implement PET data enhanced liver segmentation with CT. We utilize the difference in FDG uptake between the liver and adjacent organs to separate the liver from these structures, which have similar intensities in low-contrast CT. The relatively high normal FDG uptake, and hence high SUV of liver metabolism, allows an accurate estimation for liver segmentation in CT images. By deformable registration, the PET ROIs are mapped onto the CT images for the initial liver segmentation in CT. To overcome the different intensity values of CT images from different patients or over multiple temporal imaging sessions, the initial liver region in CT images is used to establish the accurate threshold criteria for CT liver segmentation. To prevent the deformable model from leaking into the adjacent tissues and structures, the feature images are computed to exclude and disconnect neighboring organs and tissues from liver. Our experimental results in 12 clinical PET-CT studies suggest that our algorithm is robust when dealing with livers of different shapes and sizes and from a range of different patients.

Due to the rapid advances in dual-modality PET-CT scanners, the access to and assimilation of critical information within these data are becoming extremely difficult with conventional 2D and even with sophisticated 3D visualization. In this study, we propose an integration of maximum intensity projection (MIP) and direct volume rendering (DVR) algorithms to provide a comprehensive solution for PET-CT visualization. In this process, interactive selection of a point-of-interest (POI) from fused-MIP of PET-CT is used to automate the image enhancements, such as transfer function generation, for subsequent DVR visualization used in further image diagnosis. Such automation can significantly ease the required manual image enhancements/manipulation that must be performed during image visualization. We demonstrate the effectiveness of our proposed volume rendering visualization scheme with its application to clinical PET-CT tumor studies.

Functional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our recently proposed adaptive fuzzy clustering (AFC) technique, which applies standard fuzzy clustering to sub-divided data. Monte Carlo simulations were performed to generate noisy dynamic SPECT data with quantitative analysis for the fitting using the general linear least square method (GLLS) and enhanced model-aided GLLS methods. The results show that AFC substantially improves computational efficiency and obtains improved reliability as standard fuzzy clustering in estimating parametric images but is prone to slight underestimation. Normalization of tissue time activity curves may lead to severe overestimation for small structures when AFC is applied.

Dual modality PET/CT has now essentially replaced PET in clinical practice and provided an opportunity to improve image segmentation through the high resolution, lower noise CT data. Thus far most research efforts have concentrated on segmentation of PET-only data. In this work we propose a systematic solution for the automated segmentation of brain PET/CT images into gray, white matter and CSF regions with the MAP-MRF model. Our approach takes advantage of the full information available from the combined scan. A PET/CT image pair and its segmentation result are modelled as a random field triplet, and segmentation is eventually achieved by solving a maximum a posteriori (MAP) problem using the expectation-maximization (EM) algorithm with simulated annealing. We compared the novel algorithm to two widely used PET-only based segmentation methods in the SPM5 toolbox and the VBM toolbox for simulation and patient data. Our results suggest that using the proposed approach substantially improves the accuracy of the delineation of brain structures.

Automatic segmentation and detection of lungs and tumors in FDG PET-CT images is potentially beneficial in the diagnosis and staging of patients with non-small cell lung cancer (NSCLC). However, simultaneous lung segmentation and tumor detection is not a trivial task, particularly due to noise in the datasets, proximity of the lung lesion to the mediastinum and chest wall in certain instances, and disease involvement of non-enlarged lymph nodes.

Nowadays power quality, especially voltage quality becomes more and more important for utilities, manufacturers and customers as well as for the regulatory authorities. Therefore a growing number of voltage quality monitoring campaigns with continuously increasing measurement times, considerably longer than one week, are observed. The assessment of voltage quality according to common standards (e.g. IEC 61000-2-x or EN 50160) is usually based on an evaluation interval of one week while the handling of measurements lasting more than a week is not clearly specified. Furthermore the measurement data contain lots of information that is not used by the actual applied assessment methods in the most efficient way. Especially for internal planning purposes within the utilities the knowledge of medium- and long-term trends and variations as well as information about the typical time-dependent customer behavior can be very useful. The paper presents a method that includes medium- and long- term variations (e.g. seasonal variations) of continuous voltage quality parameters (e.g. harmonics) into the calculation of actual levels. The method is based on statistical tolerance bounds and produces more reliable values that can be compared with limits given by actual standards or used for further analyses. After a description of actual assessment methods the paper gives a systematical overview on the variations of voltage quality parameters and typical reasons for that. Next the statistical basics are explained on several examples. Based on the experiences of the authors finally some recommendations for practical application of the suggested method are given.

The increasing size of medical image archives and the complexity of medical images have led to the development of medical content-based image retrieval (CBIR) systems. These systems use the visual content of images for image retrieval in addition to conventional textual annotation, and have become a useful technique in biomedical data management. Existing CBIR systems are typically designed for use with single-modality images, and are restricted when multi-modal images, such as co-aligned functional positron emission tomography and anatomical computed tomography (PET/CT) images, are considered. Furthermore, the inherent spatial relationships among adjacent structures in biomedical images are not fully exploited. In this study, we present an innovative retrieval system for dual-modality PET/CT images by proposing the use of graph-based methods to spatially represent the structural relationships within these images. We exploit the co-aligned functional and anatomical information in PET/CT, using attributed relational graphs (ARG) to represent both modalities spatially and applying graph matching for similarity measurements. Quantitative evaluation demonstrated that our dual-modal ARG enabled the CBIR of dual-modality PET/CT. The potential of our dual-modal ARG in clinical application was also explored.

We present a brief overview of past and future applications of high performance computing in biofilm modeling. In particular we show that even relative simple two-dimensional models lead to computing expensive simulation experiments, due to data uncertainties inherent in all biofilm models.

We conduct a computational study of biofilm exposure to external detachment forces, utilising techniques from computational fluid dynamics and statistics. The results obtained for irregular arrangements of biofilm colonies are compared with those of evenly distributed colonies. This requires a multitude of flow field simulations in irregular domains. Our findings suggest that the biofilm community as an entity is protected better for non-uniform spacing between colonies, while the individual biofilm colony that lives under the worst condition is protected better if neighboring colonies are approximately equidistantly spaced.

It remains a challenge to identify the different types of dementia and separate these from various subtypes from the normal effects of ageing. In this paper the potential of parametric images from FDG-PET studies to aid the classification of dementia using data mining techniques was investigated. Scalar, joint, histogram and voxel-level features were used in the investigation with principal component analysis (PCA) for dimensionality reduction. The logistic regression model and the additive logistic regression model were applied in the classification. The results show that cerebral metabolic rate of glucose consumption (CMRGlc) was efficient in the classification of dementia and data mining using voxel-level features with PCA and the logistic regression model method achieving the best classification.

The accurate segmentation of PET-only brain images is challenging because of the low spatial resolution and high noise level in PET data. PET/CT has now replaced PET and offers the opportunity to improve segmentation through the high resolution, lower noise CT data. This paper pioneers the research of PET-CT brain image segmentation, which takes advantage of the full information available from the combined scan. In the proposed approach, the contrast stretched CT image is utilized to delineate cerebrospinal fluid (CSF) from other brain tissues. Gray matter is separated from white matter by applying the fuzzy clustering of spatial patterns (FCSP) algorithm to the joint PET-CT image. We compared our approach to a widely used PET segmentation method in the SPM toolbox for simulation and patient data. Our results prove that the incorporation of anatomical information in CT images substantially improves the accuracy of brain structure delineation.

PET-CT imaging has shown its superiority in the clinical management of cancer. The markedly increased amount of imaging data have given rise to the development of computer-aided diagnosis (CAD) to aid the clinician in the interpretation of large volumes of data. The delineation of anatomical structures is one of the major components of CAD. Currently, the majority of segmentation methods are focused on the segmentation of organs and tissues using high-contrast anatomical images such as high-dose CT with injected contrast agent. However, typically low-dose CT protocol without the use of contrast agent are used in PET-CT studies, which leads to low-contrast CT images with a relatively high level of noise. This study investigated the potential of using information extracted from the co-registered PET-CT data in the segmentation of anatomical structures. A preliminary knowledge-based system was developed to process eight clinical PET-CT studies for lung cancer. The results of qualitative and quantitative analysis demonstrate the efficiency of incorporating the information derived from co-registered structural and functional images in the segmentation of anatomical structures for whole body PET-CT studies. It also implies that the methods relying on the HU value, like thresholding, are incapable of accurately delineating those organs suffering from high-level noise with unclear boundary. Further investigation using advanced technologies are warranted to achieve accurate segmentation for PET-CT imaging.

Dual time point imaging has been proposed as a means of improving the accuracy of Fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) in the diagnosis of lung malignancy. However, various sampling schedules have been used, which makes direct comparisons between their results difficult. It is unclear whether these schedules have the same accuracy for the diagnosis of lung malignancy, limiting the further development and adoption of these techniques. Although theoretically malignant and benign lesions show increasing difference in FDG uptake with longer uptake periods, the increasing noise due to decay may counteract this advantage and increase variability. In this paper, a method for the design of an optimal dual time point 18F-FDG PET imaging protocol has been proposed to address this issue.

Advances in dual-modality imaging that combine anatomical and functional images have considerably improved tumor staging and treatment planning. PET/CT can, for example, detect tumor invasion into adjacent tissues, as well as provide precise localization of lesions, even when no morphological changes are identified by CT. In lung cancer staging and therapy planning, determination of a tumor's size, its invasion into adjacent structures, mediastinal node status, and the detection of distant metastases are of great importance. In this study, we investigated the use of anatomical priors in the segmentation of tumors in simulated functional PET images of the lungs. The anatomical information was used as priors to extract the lung structures from the co-aligned PET data. The performance of a conventional iterative pixel-classification algorithm of fuzzy c-means (FCM) cluster analysis for segmenting the PET data with and without the use of the priors was quantitatively evaluated. A Monte Carlo simulation of PET with anatomical priors derived from the Zubal whole-body phantom was used in the evaluation. We demonstrate that the use of the anatomical priors to restrict the PET data to regions of interest consisting only of lung structures is able to improve the accuracy and reliability of the cluster analysis segmentation of lung tumors in PET images.

The generalized linear least squares (GLLS) method has been shown to successfully construct unbiased parametric images from dynamic positron emission tomography (PET). However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) can give rise to unsuccessful voxel-wise fitting using GLLS, resulting in physiologically meaningless estimates, such as negative kinetic parameters for compartment models. In this study, three approaches were investigated to improve the reliability of GLLS applied to dynamic SPECT data. The simulation and experimental results showed that GLLS with the aid of Bootstrap Monte Carlo method proved successful in generating parametric images and preserving all of the major advantages of all the originally GLLS method, although at the expense of increased computation time.

Segmentation of multi-dimensional functional positron emission tomography (PET) studies into regions of interest (ROI) exhibiting similar temporal behavior is useful in diagnosis and evaluation of neurological images. Quantitative evaluation plays a crucial role in measuring the segmentation algorithm's performance. Due to the lack of "ground truth" available for evaluating segmentation of clinical images, automated segmentation results are usually compared with manual delineation of structures which is, however, subjective, and is difficult to perform. Alternatively, segmentation of co-registered anatomical images such as magnetic resonance imaging (MRI) can be used as the ground truth to the PET segmentation. However, this is limited to PET studies which have corresponding MRI. In this study, we introduce a framework for the objective and quantitative evaluation of functional PET study segmentation without the need for manual delineation or registration to anatomical images of the patient. The segmentation results are anatomically standardized to a functional brain atlas, where the segmentation of the corresponding MRI reference atlas image is used as the ground truth. We illustrate our evaluation framework by comparing the performance of two pixel-classification techniques based on k-means and fuzzy c-means cluster analysis, applied to clinical dynamic human brain PET studies. The experimental results show that the proposed evaluation framework is able to provide objective measures for segmentation comparison and performance.

E-health is greatly impacting on information distribution and availability within the health services, hospitals and to the public. Previous research has addressed the development of system architectures with the aim of integrating the distributed and heterogeneous medical information systems. Easing the difficulties in the sharing and management of multimedia medical data and the timely accessibility to these data are critical needs for health care providers. We have proposed a client-server agent that integrates and allows a portal to every permitted information system of the hospital that consists of picture archiving and communication systems (PACS), radiology information system (RIS) and hospital information system (HIS) via the intranet and the Internet. Our proposed agent enables remote access into the usually closed information system of the hospital and a server that manages all the multimedia medical data and allows for in-depth and complex search queries for content access and automatic creation of patient reports for distribution.

It is shown how a D-server used in the private networking environment can be used to increase the value of a private network by routing excess traffic (incremental peak traffic) via the public ISDN (integrated service digital network) and paying only for the time used. It is shown how a D-server can be used to increase the availability of the private network through bandwidth restoral and how a D-server provides the feature enhancements to non-ISDN PBXs by acting as the surrogate for the PBX in the ISDN environment. Thus, an integrated ISDN D-server can be used on an intelligent node multiplexer in a private T1 network to form a hybrid network, providing enhanced connection, capabilities for increased reliability, accessing ISDN services, and reducing costs. >

A quantitative SPECT (single photon emission computed tomography) regime based on simultaneous acquisition of emission and transmission data has been developed and validated. Transmission data are acquired using a scanning collimated line source and incorporated into a modified convolution-subtraction scatter correction method which produces transmission-dependent scatter fractions. This method tailors scatter fraction to attenuation path length, falling to zero outside the body. Following scatter correction, emission data are reconstructed using filtered backprojection, and attenuation correction is performed in a two-step procedure using reconstructed narrow-beam attenuation data from the line source measurements. The quantitative accuracy of this regime has been assessed in heart and lung phantom distributions, as well as in a human-lung perfusion scan with /sup 99m/Tc-macroaggregates. The use of simultaneously acquired transmission data for scatter and attenuation correction yields accurate results and promises great potential for practical quantitative SPECT. >

Two independent scatter correction techniques, transmission dependent convolution subtraction (TDCS) and the triple-energy window (TEW) method, were evaluated in terms of quantitative accuracy and noise properties using Monte Carlo simulation (EGS4). Emission projections (primary, scatter and scatter plus primary) were simulated for /sup 99m/Te and /sup 201/Tl for numerical chest phantoms. Data were reconstructed with an ordered-subset ML-EM algorithm including attenuation correction using the transmission data. In the chest phantom simulation, TDCS provided better S/N than TEW, and better accuracy, i.e., 1.0% vs. -7.2% in myocardium, and -3.7% vs. -30.1% in the ventricular chamber for /sup 99m/Tc with TDCS and TEW, respectively. For /sup 201/Tl, TDCS provided good visual and quantitative agreement with a simulated true primary image without noticeably increasing the noise after scatter correction. Overall the TDCS proved to be more accurate and less noisy than TEW, facilitating quantitative assessment of physiological functions with SPECT.

Our aim is to design a PET/SPECT scanner with sufficient sensitivity to support reconstruction of parameter estimates at high spatial resolution in small animals. Therefore, we studied the influence of tomograph sensitivity on parameter estimation at the pixel level. A rat brain section was segmented into striatum and cerebral cortex, kinetics were assigned to each region and the images forward projected. Tracers simulated included: (a) one with high striatal uptake and rapid exchange between plasma and the free compartment, and (b) one with moderate striatal uptake and slower exchange between plasma and the free compartment. Both tracers were simulated assuming a typical injected dose of 10 MBq and a reduced dose of 1 MBq, providing peak striatal uptake ranging from 0.05-1% injected dose/g. Sinograms were scaled to realistic count rates based on Monte Carlo simulation and a component-based count rate model. The EM parametric image reconstruction algorithm was used to form images of binding potential (BP) and bias and variance were calculated as a function of effective sensitivity (ES). ES of 2% produced low bias (

This paper presents the design of a four-legged walking machine with a versatile leg construction. It offers the possibility to investigate reptile- and mammal-like walking with only one mechanical realization. A flexible spine will support walking with better stability. For the control of the machine, a hierarchical organized control hardware will be used, which has been very successful on our other walking machines.