Determination of pore space shape and size in porous systems using NMR diffusometry. Beyond the short gradient pulse approximation

The influence of finite length gradient pulses on NMR diffusion experiments on liquids confined to diffuse between two parallel planes is investigated. It is experimentally verified that the pore size decreases when determined using finite gradient pulses if the results are analyzed within the short gradient pulse approximation. The results are analyzed using the matrix formulation. The observed minima in the echo decay profiles are considerably less sharp than theoretical analysis would indicate and we suggest that this is due to the presence of a distribution of pore sizes in the sample. In addition, effects due to the presence of background gradients are discussed. It is argued that effects due to the finite length gradient pulses are relatively minor and in realistic applications the effects due to inhomogeneities in pore sizes and effects due to background gradients will constitute more serious problems in pore size determinations by means of NMR diffusometry.     

Generation of depth-perception information in stereoscopic nuclear magnetic resonance imaging by non-linear magnetic field gradients

Stereoscopic NMR images have been produced in the past. However, because of the gradients are linear, only isometric projections can be produced, i.e., they do not carry correct depth-perception information. The resulting stereoscopic image will not have correct relative sizes at different depths. This paper gives an analysis of what perception information is needed and shows that it can be produced by a non-linear magnetic field gradient. The concept is exemplified by simulations and its implementation is demonstrated successfully by experiments. The depth-encoding gradient can be generated by static steel pieces or by current loops. The procedure can be incorporated into any existing hardware and pulse sequences, and has potential application in surgery.     

A particle filtering approach for spatial arrival time tracking in ocean acoustics

The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering.     

Modeling nonlinear ultrasound propagation in heterogeneous media with power law absorption using a k-space pseudospectral method

The simulation of nonlinear ultrasound propagation through tissue realistic media has a wide range of practical applications. However, this is a computationally difficult problem due to the large size of the computational domain compared to the acoustic wavelength. Here, the k-space pseudospectral method is used to reduce the number of grid points required per wavelength for accurate simulations. The model is based on coupled first-order acoustic equations valid for nonlinear wave propagation in heterogeneous media with power law absorption. These are derived from the equations of fluid mechanics and include a pressure-density relation that incorporates the effects of nonlinearity, power law absorption, and medium heterogeneities. The additional terms accounting for convective nonlinearity and power law absorption are expressed as spatial gradients making them efficient to numerically encode. The governing equations are then discretized using a k-space pseudospectral technique in which the spatial gradients are computed using the Fourier-collocation method. This increases the accuracy of the gradient calculation and thus relaxes the requirement for dense computational grids compared to conventional finite difference methods. The accuracy and utility of the developed model is demonstrated via several numerical experiments, including the 3D simulation of the beam pattern from a clinical ultrasound probe.     

Characterization of classical Gaussian processes using quantum probes

We address the use of a single qubit as a quantum probe to characterize the properties of classical noise. In particular, we focus on the characterization of classical noise arising from the interaction with a stochastic field described by Gaussian processes. The tools of quantum estimation theory allow us to find the optimal state preparation for the probe, the optimal interaction time with the external noise, and the optimal measurement to effectively extract information on the noise parameter. We also perform a set of simulated experiments to assess the performances of maximum likelihood estimator, showing that the asymptotic regime, where the estimator is unbiased and efficient, is approximately achieved already after few thousands repeated measurements on the probe system.     

DYNAMIC STIFFNESS METHOD FOR CIRCULAR STOCHASTIC TIMOSHENKO BEAMS: RESPONSE VARIABILITY AND RELIABILITY ANALYSES

The problem of characterizing response variability and assessing reliability of vibrating skeletal structures made up of randomly inhomogeneous, curved/straight Timoshenko beams is considered. The excitation is taken to be random in nature. A frequency-domain stochastic finite element method is developed in terms of dynamic stiffness coefficients of the constituent stochastic beam elements. The displacement fields are discretized by using frequency- and damping-dependent shape functions. Questions related to discretizing the inherently non-Gaussian random fields that characterize beam elastic, mass and damping properties are considered. Analytical methods, combined analytical and simulation-based methods, direct Monte Carlo simulations and simulation procedures that employ importance sampling strategies are brought to bear on analyzing dynamic response variability and assessment of reliability. Satisfactory performance of approximate solution procedures outlined in the study is demonstrated using limited Monte Carlo simulations.     

Application of the maximum likelihood principle to separate exponential terms in T2 relaxation of nuclear magnetic resonance

The method of maximum likelihood has been implemented for the estimation of multiple exponential components of T2 decay curves in spin echo NMR measurements on biologic tissues. Each Each component contributes an exponential term described by two parameters (initial amplitude and T2) to the T2 decay curve. The maximum likelihood method estimates the parameters and their standard errors for all terms simultaneously, avoiding the subjectivity inherent in methods such as graphical peeling. In the model used, it was assumed that water protons are compartmentalized and that the measured spin echo signals from the protons undergoing relaxation obey the Poisson distribution. A system of non-linear equations was derived and solved iteratively for the values of the exponential parameters which maximize the likelihood of obtaining the observed data under these assumptions. The approach was implemented for bi- and tri-exponential models on a MicroVAX II computer (Digital Equipment Corporation, Maynard, MA). Simulations of bi- and tri-exponential data, with and without system noise, were analyzed to assess the accuracy and reproducibility of the method. A subset of the simulations was repeated with non-linear least squares techniques and was compared to the results obtained with maximum likelihood. Rabbit muscle and gerbil brain samples were measured and analyzed with the maximum likelihood method. The simulations showed that within specific limits on relative sizes and relaxation rates of components, these parameters can be estimated with errors less than 5%. The comparison to non-linear least squares analysis showed that the maximum likelihood method is generally superior in estimating the parameters in difficult cases. The results from tissue measurements demonstrate that the method is effective even in cases where graphical peeling would clearly not yield reliable results.     

Analytical energy gradients for dynamical simulations of trans polyacetylene chains within the Pariser-Parr-Pople Hamiltonian

Exact analytical energy gradients for the Pariser-Parr-Pople (PPP) Hamiltonian are derived. A comparison of computer times for dynamical simulations of trans-Polyacetylene (t-PA) using analytical and numerical gradients is given. The numerical method is shown to lead to serious difficulties both computationally and from the point of view of numerical accuracy. In fact, using the analytical method, it turned out that the computational effort for the gradient calculation is negligible compared to that for the SCF iteration in each time step of a simulation. On the other hand, using the numerical method the gradient calculation is the time consuming bottle-neck of a simulation. A previously presented method for the gradient calculation in the Hückel type Su-Shrieffer-Heeger (SSH) Hamiltonian which was thought to be approximative, is shown to be exact.     

采用非局部主成分分析的极大似然估计图像去噪

本文提出一种采用非局部主成分分析的极大似然估计去噪方法.首先采用非局部主成分分析算法来计算像素邻域间的灰度值和纹理结构相似性,然后通过极大似然估计方法估计最优复原图像.本方法使用非局部主成分分析克服现有局部性去噪方法模糊边界等缺陷,引入极大似然估计方法来改进现有非局部均值的简单加权均值去噪处理,从而提高对图像细节信息的复原能力.最后分别使用本文方法、非局部均值和局部极大似然估计三种去噪方法,在不同噪音大小和不同几何纹理复杂度的图像中进行定性和定量的去噪实验.结果表明,本文方法可在保持图像细节和纹理信息的情况下有效去噪,较之现有方法效果更好.     

Measuring small compartments with relatively weak gradients by angular double-pulsed-field-gradient NMR

NMR diffusion–diffraction patterns observed in compartments in which restricted diffusion occurs are a useful tool for direct extraction of compartment sizes. Such diffusion–diffraction patterns may be observed when the signal intensity E_(q,?) is plotted against the wave-vector q (when q = (2π)^− 1γδG). However, the smaller the compartment sizes are, the higher are the q-values needed to observe such diffractions. Moreover, these q-values should be achieved using short gradient pulses requiring extremely strong gradient systems. The angular double-pulsed-field gradient (d-PFG) NMR methodology has been proposed as a tool to extract compartment sizes using relatively low q-values. In this study, we have used single-PFG (s-PFG) NMR and angular d-PFG NMR to characterize the size of microcapillaries of about 2 ± 1 μm in diameter. We found that these microcapillaries are characterized by relatively strong background gradients that completely masked the effects of the microscopic anisotropy (μA) of the sample, resulting in a completely unexpected E(φ) profile in the angular d-PFG NMR experiments. We also show that bipolar angular d-PFG NMR experiments can largely suppress the effect of these background gradients resulting in the expected E(φ) profile from which the compartment dimensions could be obtained with relatively weak gradient pulses. These results demonstrate that the above methodology provides a quick, reliable, non-invasive means for estimating small pore sizes with relatively weak gradients in the presence of large magnetic susceptibility.     

An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations

In recent years, there has been a growing interest in analyzing and quantifying the effects of random inputs in the solution of ordinary/partial differential equations. To this end, the spectral stochastic finite element method (SSFEM) is the most popular method due to its fast convergence rate. Recently, the stochastic sparse grid collocation method has emerged as an attractive alternative to SSFEM. It approximates the solution in the stochastic space using Lagrange polynomial interpolation. The collocation method requires only repetitive calls to an existing deterministic solver, similar to the Monte Carlo method. However, both the SSFEM and current sparse grid collocation methods utilize global polynomials in the stochastic space. Thus when there are steep gradients or finite discontinuities in the stochastic space, these methods converge very slowly or even fail to converge. In this paper, we develop an adaptive sparse grid collocation strategy using piecewise multi-linear hierarchical basis functions. Hierarchical surplus is used as an error indicator to automatically detect the discontinuity region in the stochastic space and adaptively refine the collocation points in this region. Numerical examples, especially for problems related to long-term integration and stochastic discontinuity, are presented. Comparisons with Monte Carlo and multi-element based random domain decomposition methods are also given to show the efficiency and accuracy of the proposed method.     

Solving Schrödinger Bridges via Maximum Likelihood

The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.     

MR diffusion - "diffraction" phenomenon in multi-pulse-field-gradient experiments

Using pulsed-field-gradient (PFG) experiments, the sizes of the pores in ordered porous media can be estimated from the "diffraction" pattern that the signal attenuation curves exhibit. A different diffraction pattern is observed when the experiment is extended to a larger number (N) of diffusion gradient pulse pairs. Simulations to calculate signal values from arbitrary gradient waveforms are performed for diffusion in restricted geometries using a matrix operator formalism. The simulations suggest that the differences in the characteristics of the attenuation curves are expected to make it possible to measure smaller pore sizes, to improve the accuracy of pore size measurements and potentially to distinguish different pore shapes using the N-PFG technique. Moreover, when an even number of PFG pairs is used, it is possible to observe the diffraction pattern at shorter diffusion times and measure an approximation to the average pore size even when the sample contains pores with a broad distribution of sizes.     

A fast semi-implicit method for anisotropic diffusion

Simple finite differencing of the anisotropic diffusion equation, where diffusion is only along a given direction, does not ensure that the numerically calculated heat fluxes are in the correct direction. This can lead to negative temperatures for the anisotropic thermal diffusion equation. In a previous paper we proposed a monotonicity-preserving explicit method which uses limiters (analogous to those used in the solution of hyperbolic equations) to interpolate the temperature gradients at cell faces. However, being explicit, this method was limited by a restrictive Courant–Friedrichs–Lewy (CFL) stability timestep. Here we propose a fast, conservative, directionally-split, semi-implicit method which is second order accurate in space, is stable for large timesteps, and is easy to implement in parallel. Although not strictly monotonicity-preserving, our method gives only small amplitude temperature oscillations at large temperature gradients, and the oscillations are damped in time. With numerical experiments we show that our semi-implicit method can achieve large speed-ups compared to the explicit method, without seriously violating the monotonicity constraint. This method can also be applied to isotropic diffusion, both on regular and distorted meshes.     

Suprathreshold stochastic resonance induced by ion channel fluctuation

In this study, we examine the signal detection ability of an array of neurons with intrinsic channel fluctuation. Numerical simulations show that estimation of the input signal from the output spiking activity of the neuronal array is most accurate if a proper amount of channel noise exists. Theoretical calculation of the mutual and Fisher information confirms the effect of the noise-aided information transfer in the array, or the presence of suprathreshold stochastic resonance. These results indicate that the channel noise, which induces response variability, may play an essential role in population coding.     

Speech utterance clustering based on the maximization of within-cluster homogeneity of speaker voice characteristics

This paper investigates the problem of how to partition unknown speech utterances into a set of clusters, such that each cluster consists of utterances from only one speaker, and the number of clusters reflects the unknown speaker population size. The proposed method begins by specifying a certain number of clusters, corresponding to one of the possible speaker population sizes, and then maximizes the level of overall within-cluster homogeneity of the speakers' voice characteristics. The within-cluster homogeneity is characterized by the likelihood probability that a cluster model, trained using all the utterances within a cluster, matches each of the within-cluster utterances. To attain the maximal sum of likelihood probabilities for all utterances, the proposed method applies a genetic algorithm to determine the cluster in which each utterance should be located. For greater computational efficiency, also proposed is a clustering criterion that approximates the likelihood probability with a divergence-based model similarity between a cluster and each of the within-cluster utterances. The clustering method then examines various legitimate numbers of clusters by adapting the Bayesian information criterion to determine the most likely speaker population size. The experimental results show the superiority of the proposed method over conventional methods based on hierarchical clustering.     

Quantifying Information Transmission in Eukaryotic Gradient Sensing and Chemotactic Response

Eukaryotic cells are able to sense shallow chemical gradients by surface receptors and migrate toward chemoattractant sources. The accuracy of this chemotactic response relies on the ability of cells to infer gradients from the heterogeneous distribution of receptors bound by diffusing chemical molecules. Ultimately, the precision of gradient sensing is limited by the fluctuations of signaling components, including the stochastic receptor occupancy and noisy intracellular processing. Viewing the system as a Markovian communication channel, we apply techniques from information theory to derive upper bounds on the amount of information that can be reliably transmitted through a chemotactic cell. Specifically, we derive an expression for the mutual information between the gradient direction and the spatial distribution of bound receptors. We also compute the mutual information between the gradient direction and the motility direction using three different models for cell motion. Our results can be used to quantify the information loss during the various stages of directional sensing in eukaryotic chemotaxis.     

Quantitative characterization of tissue microstructure with temporal diffusion spectroscopy

The signals recorded by diffusion-weighted magnetic resonance imaging (DWI) are dependent on the micro-structural properties of biological tissues, so it is possible to obtain quantitative structural information non-invasively from such measurements. Oscillating gradient spin echo (OGSE) methods have the ability to probe the behavior of water diffusion over different time scales and the potential to detect variations in intracellular structure. To assist in the interpretation of OGSE data, analytical expressions have been derived for diffusion-weighted signals with OGSE methods for restricted diffusion in some typical structures, including parallel planes, cylinders and spheres, using the theory of temporal diffusion spectroscopy. These analytical predictions have been confirmed with computer simulations. These expressions suggest how OGSE signals from biological tissues should be analyzed to characterize tissue microstructure, including how to estimate cell nuclear sizes. This approach provides a model to interpret diffusion data obtained from OGSE measurements that can be used for applications such as monitoring tumor response to treatment in vivo.     

Scaling behavior of threshold epidemics

We study the classic Susceptible-Infected-Recovered (SIR) model for the spread of an infectious disease. In this stochastic process, there are two competing mechanism: infection and recovery. Susceptible individuals may contract the disease from infected individuals, while infected ones recover from the disease at a constant rate and are never infected again. Our focus is the behavior at the epidemic threshold where the rates of the infection and recovery processes balance. In the infinite population limit, we establish analytically scaling rules for the time-dependent distribution functions that characterize the sizes of the infected and the recovered sub-populations. Using heuristic arguments, we also obtain scaling laws for the size and duration of the epidemic outbreaks as a function of the total population. We perform numerical simulations to verify the scaling predictions and discuss the consequences of these scaling laws for near-threshold epidemic outbreaks.     

Numerical simulations of short-mixing-time double-wave-vector diffusion-weighting experiments with multiple concatenations on whole-body MR systems

Double- or two-wave-vector diffusion-weighting experiments with short mixing times in which two diffusion-weighting periods are applied in direct succession, are a promising tool to estimate cell sizes in the living tissue. However, the underlying effect, a signal difference between parallel and antiparallel wave vector orientations, is considerably reduced for the long gradient pulses required on whole-body MR systems. Recently, it has been shown that multiple concatenations of the two wave vectors in a single acquisition can double the modulation amplitude if short gradient pulses are used. In this study, numerical simulations of such experiments were performed with parameters achievable with whole-body MR systems. It is shown that the theoretical model yields a good approximation of the signal behavior if an additional term describing free diffusion is included. More importantly, it is demonstrated that the shorter gradient pulses sufficient to achieve the desired diffusion weighting for multiple concatenations, increase the signal modulation considerably, e.g. by a factor of about five for five concatenations. Even at identical echo times, achieved by a shortened diffusion time, a moderate number of concatenations significantly improves the signal modulation. Thus, experiments on whole-body MR systems may benefit from multiple concatenations.