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At least 181 records · Page 10Linked to original sources

Optimal radiation beam profiles considering uncertainties in beam patient alignment.

The often large uncertainties that exist in beam patient alignment during radiation therapy may require modification of the incident beams to ensure an optimal delivered dose distribution to the target volume. This problem becomes increasingly severe when the required dose distribution of the incident beams becomes more heterogeneous. A simple analytical formula is derived for the case when the fraction number is high, and the desired relative dose variations are small. This formula adjusts the fluence distribution of the incident beam so that the resultant dose distribution will be as close as possible to the desired one considering the uncertainties in beam patient alignment. When sharp dose gradients are important, for instance at the border of the target volume, the problem is much more difficult. It is shown here that, if the tumor is surrounded by organs at risk, it is generally best to open up the field by about one standard deviation of the positional uncertainty--that is sigma/2 on each side of the target volume. In principle it is simultaneously desirable to increase the prescribed dose by a few per cent compared to the case where the positional uncertainty is negligible, in order to compensate for the rounded shoulders of the delivered dose distribution. When the tissues surrounding the tumor no longer are dose limiting even larger increases in field size may be advantageous. For more critical clinical situations the positional uncertainty may even limit the success of radiotherapy. In such cases one generally wants to create a steeper dose distribution than the underlying random Gaussian displacement process allows. The problem is then best handled by quantifying the treatment outcome under the influence of the stochastic process of patient misalignment. Either the coincidence with the desired dose distribution, or the expectation value of the probability of achieving complication-free tumor control is maximized under the influence of this stochastic process. It is shown that the most advantageous treatment is to apply beams that are either considerably widened or slightly widened and over flattened near the field edges for small and large fraction numbers respectively.

Dose-Response Relationship, Radiation↗

Testing the Gaussianity of the human EEG during anesthesia.

The Gaussian properties of human EEGs, which were measured over various stages of general anesthesia, were tested. The basis of the method was to describe the EEG signals by autoregressive models and to test the normality of the regression residuals with the Shapiro-Wilk statistic. The results show that in general the human EEG during anesthesia can be considered as a realization of a Gaussian stochastic process.

Anesthesia, General↗

[Application of Burg algorithm in time-frequency analysis of Doppler blood flow signal based on AR modeling].

The Doppler blood flow signal is inherently a nonstationary Gaussian random process whose time-frequency representation associates with the time-varying velocity of blood flow and its variations. With the assumption that the signal being analyzed is stationary during a short time interval, we can not get Doppler time-frequency representations with satisfactory time and frequency resolution. AR modeling based on Levinson-Durbin algorithm has been used to generate time-frequency representations of Doppler blood flow signals. But the errors of the parameters computed by the algorithm will be aggrandized by the shortening of the time interval. Burg has advanced an algorithm, which computes the parameters by making the sum of forward and backward forecasting errors minimum. In the paper, time-frequency representations computed by Burg and Levinson-Durbin algorithm were compared with the theoretical representation. We found that the time-frequency representations computed by Burg algorithm are more similar to the theoretical representation, especially in frequency band.

Algorithms↗

[Stochastic models of neuronal activity].

Techniques for modeling of a stochastic activity of neurons are briefly reviewed. Our model is proposed, some experimental results with input Gaussian stochastic processes are discussed, and the concept of e-curves is introduced.

Models, Neurological↗

[A model of stochastic activity of the neuron].

Techniques for modeling of a stochastic activity of neurons are briefly reviewed. Our model is proposed, some experimental results with input Gaussian stochastic processes are discussed, and the concept of e-curves is introduced.

Action Potentials↗

Implications of fluctuations in substitution rates: impact on the uncertainty of branch lengths and on relative-rate tests.

Many tests of the lineage dependence of substitution rates, computations of the error of evolutionary distances, and simulations of molecular evolution assume that the rate of evolution is constant in time within each lineage descended from a common ancestor. However, estimates of the index of dispersion of numbers of mammalian substitutions suggest that the rate has time-dependent variations consistent with a fractal-Gaussian-rate Poisson process, which assumes common descent without assuming rate constancy. While this model does not affect certain relative-rate tests, it substantially increases the uncertainty of branch lengths. Thus, fluctuations in the rate of substitution cannot be neglected in calculations that rely on evolutionary distances, such as the confidence intervals of divergence times and certain phylogenetic reconstructions. The fractal-Gaussian-rate Poisson process is compared and contrasted with previous models of molecular evolution, including other Poisson processes, the fractal renewal process, a Lévy-stable process, a fractional-difference process, and a log-Brownian process. The fractal models are more compatible with mammalian data than the nonfractal models considered, and they may also be better supported by Darwinian theory. Although the fractal-Gaussian-rate Poisson process has not been proven to have better agreement with data or theory than the other fractal models, its Gaussian nature simplifies the exploration of its impact on evolutionary distance errors and relative-rate tests.

Amino Acid Substitution↗

Probabilistic dynamics of some jump-diffusion systems.

Some exact solutions to the forward Chapman-Kolmogorov equation are derived for processes driven by both Gaussian and compound Poisson (shot) noise. The combined action of these two forms of white noise is analyzed in transient and equilibrium conditions for different jump distributions and additive Gaussian noise. Steady-state distributions with power-law tails are obtained for exponentially distributed jumps and multiplicative linear Gaussian noise. Two applications are discussed: namely, the virtual waiting-time or Takàcs process including Gaussian oscillations and a simplified model of soil moisture dynamics, in which rainfall is modeled as a compound Poisson process and fluctuations in potential evapotranspiration are Gaussian.

Journal Article↗

On the relationship between power mode and pressure amplitude decorrelation.

Estimation of mean transit time, along with tissue blood volume, are important factors in determining soft tissue perfusion. Recently, power mode decorrelation techniques have been successfully used to estimate mean transit time of red blood cells or contrast material through a region-of-interest (ROI) both in laminar flow phantoms and in vivo. The previously described theory for power mode decorrelation derives from a phenomenological stochastic differential equation (Langevin equation) based on conservation of matter, relating the detected signal power to the measured rate of decorrelation. Given the experimental support for power mode decorrelation as a method to estimate mean transit time, it becomes important to determine the relationship between the phenomenological parameters that appear in the corresponding stochastic equation and system parameters, such as the transducer point response function. With this equation as a starting point, and using the fact that the pressure amplitude is a Gaussianly distributed random process, the following stochastic differential equation for the pressure amplitude p(t) is derived, a necessary first step in establishing the relationship between the measured decorrelation rate and system parameters (i.e., point response function): dp(t)/dt = -(v/2+2ik x v)p(t)+f(t), where v/2 represents the rate of decorrelation, 2k x v is the Doppler shift for an insonating wave vector k and particle velocity v.f(t) is a stationary, white noise Gaussian random process.

Blood Pressure↗

Rotation-invariant texture retrieval with Gaussianized steerable pyramids.

This paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alpha-stable sub-Gaussian model to capture their non-Gaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotation-invariant version of the Kullback-Leibler Divergence between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles.

Algorithms↗

Probability density of the surface electromyogram and its relation to amplitude detectors.

When the surface electromyogram (EMG) generated from constant-force, constant-angle, nonfatiguing contractions is modeled as a random process, its density is typically assumed to be Gaussian. This assumption leads to root-mean-square (RMS) processing as the maximum likelihood estimator of the EMG amplitude (where EMG amplitude is defined as the standard deviation of the random process). Contrary to this theoretical formulation, experimental work has found the signal-to-noise-ratio [(SNR), defined as the mean of the amplitude estimate divided by its standard deviation] using mean-absolute-value (MAV) processing to be superior to RMS. This paper reviews RMS processing with the Gaussian model and then derives the expected (inferior) SNR performance of MAV processing with the Gaussian model. Next, a new model for the surface EMG signal, using a Laplacian density, is presented. It is shown that the MAV processor is the maximum likelihood estimator of the EMG amplitude for the Laplacian model. SNR performance based on a Laplacian model is predicted to be inferior to that of the Gaussian model by approximately 32%. Thus, minor variations in the probability distribution of the EMG may result in large decrements in SNR performance. Lastly, experimental data from constant-force, constant-angle, nonfatiguing contractions were examined. The experimentally observed densities fell in between the theoretic Gaussian and Laplacian densities. On average, the Gaussian density best fit the experimental data, although results varied with subject. For amplitude estimation, MAV processing had a slightly higher SNR than RMS processing.

Adolescent↗

[A computer system for image feature analysis of chest radiographs in CT-documented interstitial lung diseases].

This paper reports the clinical significance of a computer-analyzing system to detect and characterize interstitial lung diseases in chest radiographs. One hundred and sixty-four ROIs were selected in the right lungs of 41 patients with normal and those of 41 with diffuse interstitial involvement proved by X-ray CT. Selected ROIs were processed by 4-directional Laplacian-Gaussian filtering, binarization, and determination of linear shadows. For quantitative analysis of interstitial shadows, radiographic index, normalized percent-area of shadows in a ROI, was determined and evaluated in the images. Then, the radiographic indices were compared with CT-documented characteristics of interstitial lung shadows. The results were as follows: 1) Abnormal and normal lungs were well differentiated each other by all kinds of the radiographic indices obtained from the images filtered by 4-directional Laplacian-Gaussian filters and from those processed by determination of linear shadows. 2) ROIs with honeycombing shadows and with other interstitial shadows (interstitial changes other than honeycombing and nodulation) shown in CT were differentiated each other by the radiographic indices obtained from the summation image and the vertical directional image processed by determination of linear shadows (p less than .01). However, ROIs with multiple nodular shadows and with other interstitial shadows were not classified by these radiographic indices. These results indicate that this system may be useful for detection and characterization of interstitial diseases in chest radiographs.

Adult↗

Deterministic and stochastic processes in children's isometric force variability.

This study examined the influence of deterministic and stochastic processes (including white Gaussian noise) on reductions in the amount of force output variability through childhood. The structure of the force signal produced during a constant isometric pinch grip task was examined as a function of age (6, 8, and 10 years, and young adults), availability of feedback information (with and without vision), digit (thumb and index finger), and force level (5, 15, 25, and 35% of maximal voluntary contraction). The amount of white Gaussian noise in the force signals was negligible and not age related. The availability of vision led increasingly over the older age groups to lower long-range correlations with more than a single scaling range in a 1/f-like decay process. The reductions in the amount of force variability from childhood to adulthood were related in large part to deterministic organization that increased the adaptive use of higher frequency components, due to the more flexible use of information feedback and feedforward processes.

Analysis of Variance↗

Stochastic radial basis functions.

Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can closely approximate gaussian activation functions which improve with the number of states in the counter. The means and variances of these gaussian approximations can be controlled by varying the output combinational logic function of the binary counter variables.

Binomial Distribution↗

The use of the Gaussian curve fitting method for scintigraphic measurements of the swallowing process in healthy subjects: implications for evaluation of dysphagia.

OBJECTIVE: To present the results of scintigraphic evaluation, using the Gaussian curve fitting method, via 3 parameters of oropharyngeal swallow: (1) pharyngeal transit time, (2) premature pharyngeal entry, and (3) postswallow pharyngeal stasis while ingesting liquid. DESIGN: A descriptive study. SETTING: A rehabilitation hospital affiliated with a medical university. PARTICIPANTS: Eighteen healthy subjects. INTERVENTION: All 18 subjects received scintigraphic swallow examination to evaluate dynamic swallow process of 5 mL of liquid. MAIN OUTCOME MEASURES: The Gaussian curve fitting method was used to calculate the pharyngeal transit time, premature pharyngeal entry, and postswallow pharyngeal stasis. RESULTS: The mean pharyngeal transit time was .71 seconds. The maximal percentage of premature pharyngeal entry was 3%. The maximal percentage of postswallow pharyngeal stasis was 9%. CONCLUSIONS: The Gaussian curve fitting can be used as an objective and time-saving method to calculate the parameters in scintigraphic swallowing examination. Our results approximate other researchers' reports.

Aged↗

Blind estimation of reverberation time.

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. Many state-of-the-art audio signal processing algorithms, for example in hearing-aids and telephony, are expected to have the ability to characterize the listening environment, and turn on an appropriate processing strategy accordingly. Thus, a method for characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, a method for estimating RT without prior knowledge of sound sources or room geometry is presented. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time-constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Acoustics↗

Q-ball imaging.

Magnetic resonance diffusion tensor imaging (DTI) provides a powerful tool for mapping neural histoarchitecture in vivo. However, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, bending, or twisting within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sampling. It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomographic inversion called the Funk-Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity. The present paper reviews the theory of q-ball imaging and describes a simple linear matrix formulation for the q-ball reconstruction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are discussed.

Algorithms↗