the concept of picture archiving and communication systems (PACS) is now widely accepted in the medical community. In order to bring the conceptto reality, however, innovative designs and implementations are needed. ...
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ISBN:
(纸本)0819402788
the concept of picture archiving and communication systems (PACS) is now widely accepted in the medical
community. In order to bring the conceptto reality, however, innovative designs and implementations are needed.
One such design is a fiber optic star based PACS, conceived by the University of Arizona and toshiba corporation.
this PACS network is based on a multiplexed passive star local area network with wavelength-division multiplexing
to provide separate logical channels for transfer of control and image data. the system consists of an image-Network
(INEt), for imagetransfer at a rate of 140 Mbps, and a Control-network (CNEt), operating at 10 Mbps, for
mediating the flow of imagetransfers. INEt is a circuit switched network where a network supervisor grants users
permission to transfer images over it, while CNEt employs the CSMA/CD protocol for bus arbitration. Before such a
system can be deployed, an accurate evaluation study must be carried outto estimate its performance characteristics.
Such evaluations are complicated both by the complexity of the PACS itself and the varied demands that are placed
on such a system. An novel approach based on siochastic aciiviiy neiworks, a stochastic extension of Petri nets, is
useful in this regard. Stochastic activity networks were used to develop a detailed model of the command and image
channels. the performance of the system was then evaluated under realistic workload conditions. In particular, we
were able to estimate a number of important performance variables including the image response time, command
channel delay, and queue length each type of node and the network supervisor. the results 1) show that stochastic
activity networks are an appropriate model type for evaluating picture archiving and communication systems, 2)
delineate the workload conditions under which PACS may effectively operate, and 3) show that even when these
conditions are exceeded, the command channel load
Unlike the attenuation densities which are estimated in conventional x-ray tomography the signals estimated and measured in magnetic resonance (MR) imaging are inherently time-varying. Any method for reconstructing th...
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ISBN:
(纸本)0819402753
Unlike the attenuation densities which are estimated in conventional x-ray tomography the signals estimated and measured in magnetic resonance (MR) imaging are inherently time-varying. Any method for reconstructing the free induction decay (FID) signals within particular voxels of an object musttake this into account. In spite of this, Fourier MR imaging methods involve decoding procedures for spatial localization of spin density and exponential decay parameters which assume thatthe signal amplitudes are constant in time, an assumption which is valid only under the condition thatthe data collection window is very small with respectto the signal decay rate. this forces conventional Fourier methods to generate images from small slices of the full FID time signal (on the order of 10msec). We have recently demonstrated in NMR spectroscopy that fitting the FID to exponentially decaying sinuosoid models with unknown amplitude,frequency and decay parameters using the method of maximum-likelihood yields far more accurate estimates of the parameters than those based on Fourier methods. this of course requires collection and fitting of the entire FID time signal. Following these ideas, we now describe a new method for reconstructing spin density and t2 images from data collected in the hydrogen MR imaging mode, which models the FID from every voxel of the image as a sinusoid with unknown amplitude and decay. the paper first presents a signal model for the FID signals collected from hydrogen MR imaging based on previous work with collaborators [2], which is atthe heart of our new algorithm for maximum likelihood estimation of the image parameters. then the major focus of this paper is to describe the maximum likelihood estimation of the spin density and t2 decay images, and present its solution via an iterative expectation maximization algorithm. Finally we show the reconstruction of a simulated 2-dimensional phantom imaged using conventional phase and frequency encoding.
Multi-spectral image data fusion techniques for tissue classification of magnetic resonance (MR) images are presented. Using MR it is possible to obtain images of proton density, the spin-lattice relaxation time const...
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ISBN:
(纸本)081940277X
Multi-spectral image data fusion techniques for tissue classification of magnetic resonance (MR) images are presented. Using MR it is possible to obtain images of proton density, the spin-lattice relaxation time constant (t2) and the spin-spin relaxation time constant (t2), of the same anatomical section of the human body. In this paper we adopt a sensor fusion approach to tissue classification and segmentation in which each of the three images is treated as the output of different sensors. Regions of the images are modeled as noncausal Gaussian Markov random fields (GMtFs) and the underlying tissue label image is also assumed to follow a Gibbs distribution. two different multi-spectral tissue labeling algorithms, maximum a posteriori (MAP) estimation and the Dempster-Shafer evidential reasoning technique, are presented. In the Bayesian MAP approach, we use an independent opinion pool for the data fusion and a deterministic relaxation to obtain the MAP solution. In practice, the Bayesian approach may be too restrictive and a likelihood represented by a point probability value is usually an overstatement of what is actually known. In the Dempster-Shafer approach, we adopt Dempster's rule of combination for data fusion, using belief intervals and ignorance to represent our confidence in a particular labeling and we present a new deterministic relaxation scheme that updates the belief intervals. Results obtained from real MR images are presented.
Color vision is nowadays an important and intensively studied field as well in the machine vision research as in the human vision research. there are numerous (three dimensional) color coordinate systems used in machi...
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ISBN:
(纸本)0819402389
Color vision is nowadays an important and intensively studied field as well in the machine vision research as in the human vision research. there are numerous (three dimensional) color coordinate systems used in machine vision, and most of them are based on simplified models of human vision. thought many times sufficient, three dimensional color representation does not give needed accuracy allways. to achieve better performance, multispectral imaging and analysis methods are needed. A pattern recognition based color analysis method, the subspace approach, is described in this paper. this method is applicable to color discrimination, recognition, and classification. Eigen-spectra information of natural colors is compared to anatomical and physiological data on color vision mechanism. Interesting similarities were observed.
Iterative Data Refinement (abbreviated IDR) is a general procedure which encompasses many special procedures for image reconstruction and for related problems. It is a procedure for estimating data that would have bee...
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ISBN:
(纸本)0819402389
Iterative Data Refinement (abbreviated IDR) is a general procedure which encompasses many special procedures for image reconstruction and for related problems. It is a procedure for estimating data that would have been collected by an idealized measuring device from data that were collected by an actual measuring device. Such approaches have been applied successfully in areas of reconstruction in x-ray tomographic radiology. In fact, IDR is general enough to encompass standard approaches to data recovery, such as the Error-Reduction and the Hybrid Input-Output methods. Along similar lines, IDR provides a common framework within which new algorithms can be developed for improved magnetic resonance imaging (MRI). We have applied and implemented the approach of IDR to a specific problem in MRI, namely to the correction of spatially-dependent blurs due to short local transverse relaxation (t2) values. the algorithm is designed to reconstructt2-weighted spin density images with improved spatial resolution. the practical computational significance of using the IDR approach will be illustrated by the reconstruction of mathematical phantoms. We have found that over-relaxation of the algorithm improves computational speed by up to a factor of five.
In a digital diagnostic imaging department, the majority of operations for handling and processing of images can be grouped into a small set of basic operations, such as image data buffering and storage, image process...
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Capacitive sensing of x-ray generated charge images on amorphous selenium layers represents an attractive technique for the electronic recording of projection radiographs. In order to evaluate the practical limits of ...
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"Help me communicate more quickly and more effectively with referring clinicians". this request was the driving force behind the installation of the At&t CommView™ System at Duke. the CommView System is ...
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We here discuss acousto-electric devices for electronicimaging of light. these devices are more versatile than line scan imaging devices in current use. they have the capability of presenting the image information in...
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the current state of medical diagnostic ultrasound imaging is analyzed including discussions of present limitations and possible solutions of these problems. Ultrasound is unique in thattomographic images are formed ...
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