Partial volume effect compensation for improved reliability of quantitative blood-brain barrier permeability

INTRODUCTION: Blood-brain barrier (BBB) plays an important role in the pathophysiology of many central nervous system disorders. In the past, a number of laboratory techniques have been proposed to quantify permeability coefficient, k(i), an important index of barrier function. Recently, MRI has been used to estimate k(i) based on the unidirectional tracer kinetics model in one compartment as proposed by Patlak et al. and has been found to be in good agreement with the gold standard quantitative autoradiography technique. Rapid data acquisition, a prerequisite of this MRI-based technique, causes a compromise in spatial resolution resulting in partial volume (PV) averaging, an effect that is seldom explicitly compensated for in quantitative neuroimaging studies. This may have profound effect on the reliability of estimates obtained using quantitative methods. Existing PV compensation techniques that use complex statistical algorithms perform corrections on stationary images. In this proof-of-principle study, the effect of PV averaging on BBB permeability coefficient has been evaluated using a simulation model, and a postprocessing technique that makes use of dynamic information has been proposed for PV compensation in order to improve the reliability of this quantitative method. MATERIALS AND METHODS: A computer simulation model is presented, which evaluates the effect of PV averaging on permeability coefficient estimates. Beginning with a known k(i), a PV compensation technique is proposed, which aims at correcting calculated k(i) to obtain the original estimate. The application of the PV compensation technique is demonstrated in a rat stroke brain model. Magnetic resonance imaging experiments were performed in Wistar rats (n=2) on a 4.7-T scanner. After acquiring localizer, T2-weighted and diffusion-weighted images, a rapid T1 mapping protocol was implemented to acquire one pre-gadolinium-diethylenetriaminepentaacetic acid baseline data set followed by a series of postinjection data sets. The data were postprocessed without and with application of PV compensation technique to obtain a k(i) estimate. RESULTS AND DISCUSSION: The issue of PV averaging as a result of limited spatial resolution is often not addressed in quantitative MRI studies. In this work, simulation experiments have provided useful insight into the PV effects on permeability coefficient estimate. The findings of the simulation experiments agree well with the results obtained from MR experiments. Results from the MR experiments suggest that it may be important to perform PV compensation in order to improve the reliability of permeability coefficient estimates. Future work involves classification of tissue component into gray and white matter and CSF to improve the accuracy of the compensation technique and to investigate repeatability of the technique in a larger group of animals.     

Tracking noisy limit cycle oscillation with nonlinear filters

For engineering systems, the dynamic state estimates provide valuable information for the detection and prediction of failure due to noise and vibration. From this perspective, nonlinear filtering techniques are applied to the problem of state estimation of dynamical systems undergoing noisy limit cycle oscillation. Specifically, the extended Kalman filter, ensemble Kalman filter and particle filter are used to track the limit cycle oscillations of a Duffing oscillator using noisy observational data. The noisy limit cycle oscillations feature highly non-Gaussian trends. The efficiency and limitations of the extended Kalman filter, ensemble Kalman filter and particle filter in tracking limit cycle oscillations are examined with respect to the model and measurement noise and sparsity of measurement data. For the limit cycle oscillations considered here, it is demonstrated that the ensemble Kalman filter and particle filter outperform the extended Kalman filter in the presence of sparse observational data or strong measurement noise. For moderate measurement noise and frequent measurement data, the ensemble Kalman filter and particle filter perform equally well in comparison to the extended Kalman filter.     

改进维纳滤波器及其在目标方位估计中的应用

针对复杂的水声环境以及信噪比较低的目标信号导致方位估计性能较差的问题,本文提出了一种基于改进维纳滤波器和波束形成器的方位估计方法,该方法能够抑制噪声,提高目标方位估计性能。首先利用改进维纳滤波器抑制各通道接收数据中的噪声,提高输出信噪比。在此基础上,将改进维纳滤波器的输出通过波束形成器,获得目标的方位估计。改进维纳滤波器能够通过调整滤波器参数,控制滤波器的噪声抑制能力和信号失真。因此,针对不同的波束形成器对信号失真的敏感程度不同,可以通过调节改进维纳滤波器的参数,获得噪声抑制与信号失真之间的最佳折中,从而提高输出信噪比,降低目标方位估计的信噪比门限和均方根误差。仿真和实验结果验证了本文方法。      

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.     

非重叠背景噪声下的自适应维纳滤波模式识别方法

维纳滤波实现模式识别的关键问题是噪声知识的获得与估计。提出一种非重叠背景噪声的提取方法,首先将对噪声的粗略估计代入维纳滤波函数,得到相关峰。然后由相关峰的位置及参考图像的尺寸确定目标图像的位置和范围,从而提取出背景噪声图像。经过二次维纳滤波,得到改善的相关输出结果,实现了自适应过程。仿真结果表明在非重叠有色噪声环境下,与噪声估计法以及传统的维纳滤波方法相比,此方案具有较好的识别效果。     

Recovering smooth dynamics from time series with the aid of recurrence plots

A graphical method based on recurrence plots is used in the reconstruction of the phase space from a time series of measurements. It is demonstrated that if the embedding delay and dimension are correctly chosen, the recurrence plot of a smooth dynamical system has a particularly simple form. It is shown how to use recurrence plots to determine the correct embedding parameters so that reliable quantitative information can be drawn about the system generating the time series. The average line length in the plot is shown to be directly related to the prediction horizon. Furthermore, it is a numerical characteristic of the embedded series independent of the threshold used in the plot.     

Recurrence plot statistics and the effect of embedding

Recurrence plots provide a graphical representation of the recurrent patterns in a timeseries, the quantification of which is a relatively new field. Here we derive analytical expressions which relate the values of key statistics, notably determinism and entropy of line length distribution, to the correlation sum as a function of embedding dimension. These expressions are obtained by deriving the transformation which generates an embedded recurrence plot from an unembedded plot. A single unembedded recurrence plot thus provides the statistics of all possible embedded recurrence plots. If the correlation sum scales exponentially with embedding dimension, we show that these statistics are determined entirely by the exponent of the exponential. This explains the results of Iwanski and Bradley [J.S. Iwanski, E. Bradley, Recurrence plots of experimental data: to embed or not to embed? Chaos 8 (1998) 861–871] who found that certain recurrence plot statistics are apparently invariant to embedding dimension for certain low-dimensional systems. We also examine the relationship between the mutual information content of two timeseries and the common recurrent structure seen in their recurrence plots. This allows time-localized contributions to mutual information to be visualized. This technique is demonstrated using geomagnetic index data; we show that the AU and AL geomagnetic indices share half their information, and find the timescale on which mutual features appear.     

Infrared/laser multi-sensor fusion and tracking based on the multi-scale model

The state estimation problem of targets detected by infrared/laser composite detection system with different sampling rates was studied in this paper. An effective state estimation algorithm based on data fusion is presented. Because sampling rate of infrared detection system is much higher than that of the laser detection system, the theory of multi-scale analysis is used to establish multi-scale model in this algorithm. At the fine scale, angle information provided by infrared detection system is used to estimate the target state through the unscented Kalman filter. It makes full use of the high frequency characteristic of infrared detection system to improve target state estimation accuracy. At the coarse scale, due to the sampling ratio of infrared and laser detection systems is an integer multiple, the angle information can be fused directly with the distance information of laser detection system to determine the target location. The fused information is served as observation, while the converted measurement Kalman filter (CMKF) is used to estimate the target state, which greatly reduces the complexity of filtering process and gets the optimal fusion estimation. The simulation results of tracking a target in 3-D space by infrared and laser detection systems demonstrate that the proposed algorithm in this paper is efficient and can obtain better performance than traditional algorithm.     

Estimating the state of large spatio-temporally chaotic systems

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points.     

Step-down infusions of Gd-DTPA yield greater contrast-enhanced magnetic resonance images of BBB damage in acute stroke than bolus injections

A rat model of transient suture occlusion of one middle cerebral artery (MCA) was used to create a unilateral reperfused cerebral ischemic infarct with blood-brain barrier (BBB) opening. Opening of the BBB was visualized and quantitated by magnetic resonance (MR) contrast enhancement with a Look-Locker T(1)-weighted sequence either following an intravenous bolus injection (n=7) or during a step-down infusion (n=7) of gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA). Blood levels of Gd-DTPA after either input were monitored via changes in sagittal sinus relaxation rate. Blood-to-brain influx constants (K(i)) were calculated by Patlak plots. On the basis of the MRI parameters and lesion size, the ischemic injury was determined to be similar in the two groups. The bolus injection input produced a sharp rise in blood levels of Gd-DTPA that declined quickly, whereas the step-down infusion led to a sharp rise that was maintained relatively constant for the period of imaging. Visual contrast enhancement and signal-to-noise (S/N) ratios were better with the step-down method (S/N=1.8) than with bolus injection (S/N=1.3). The K(i) values were not significantly different between the two groups (P>.05) and were around 0.005 ml/(g min). The major reason for the better imaging of BBB opening by the step-down infusion was the higher amounts of Gd-DTPA in plasma and tissue during most of the experimental period. These results suggest that step-down MR contrast agent (MRCA) administration schedule may be more advantageous for detection and delineation of acute BBB injury than the usually used bolus injections.     

Unscented Kalman filter and its nonlinear application for tracking a moving target

The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 40 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. To overcome these limitations, this paper proposes the unscented Kalman filter (UKF). And the algorithms of the FEKF, SEKF and UKF are given. Furthermore, the state models and measurement models of a target are set up. For comparison purpose, the three algorithms is simulated for the target tracking, and the algorithm performance is analyzed and compared by the simulation results of FEKF, SEKF and UKF. Numerical results demonstrate that FEKF and UKF give almost identical results while the estimates of SEKF are clearly worse. The UKF is easier to implement, avoiding Jacobian and Hessian matrices computation.     

Fast Estimation Algorithm for Calculation of Reflectance Map based on Wiener Estimation Technique

We propose a fast estimation algorithm for calculation of a reflectance map from measured radiances based on Wiener estimation technique. This technique uses many sample data of reflectance maps to decide transformation for accurate estimation. Reflectance maps of objects are estimated rapidly by linear operation between a vector of measured radiances and an estimation matrix previously calculated based on the sample data.     

惯性约束聚变中环孔编码图像恢复的改进维纳滤波方法

针对惯性约束聚变(ICF)中环形孔径编码图像的恢复问题提出了一种改进的维纳滤波方法。在传统的维纳滤波方法中,由于原图像和噪声是未知的,故通常是用某一待定常量来代替其中的噪声与信号的谱密度之比。这种近似忽视了信号与噪声本身的信息,从而造成丢失某些关键的细节,难以达到高质量的图像复原效果。在改进的方法中,首先采用传统维纳滤波方法求得初始估值,然后利用该初始值求得原图像及噪声的谱密度估值,进而利用这些新获得的信息构成改进的维纳滤波器对退化图像进行第二次滤波。实验表明,这种改进方法可以克服原方法的不足,突出图像的一些关键细节,提高图像的整体质量。在仿真实验中,恢复图像的均方误差降低了15％以上;在实际惯性约束聚变图像的解码恢复实验中,图像恢复效果亦有显著改善。该方法还可以推广到其他图像恢复的应用中。     

OFDM symbol detection integrated with channel multipath gains estimation for doubly-selective fading channels

Orthogonal Frequency Division Multiplexing (OFDM) is a technique for wideband transmission that is commonly used in modern wireless communication systems because of its good performance over frequency selective channels. However OFDM systems are sensitive to channel time variations resulting in Inter-Carrier Interference (ICI), that without suitable detection methods can degrade performance significantly. Channel State Information (CSI) is essential to various OFDM detection schemes, and its acquisition is a critical factor over time varying channels. This work considers a Kalman filter channel multipath gains estimation technique for time varying environments, integrated with a novel detection scheme for OFDM based on a Sphere Decoding (SD) algorithm derived to exploit the banded structure of the channel matrix. This combined scheme employs decision–feedback from the SD requiring only a low pilot symbol density, and hence improves bandwidth efficiency. Three techniques for integrating the Kalman filter operating in decision–feedback mode, with SD data detection that produces these decisions, are considered in this paper. When compared with other competing schemes, this integrated symbol detection and channel multipath gains estimation approach for OFDM provides performance advantages over time varying channels. Furthermore, it is shown that for moderate Doppler shifts the degradation that carrier phase noise induces in this scheme is small.     

实时运动结构重建在自主导航系统中的应用

实时运动结构重建是自主车辆、机器人导航、空间探测器自主降落、智能监控等领域中的重要研究课题。目前实时运动结构重建主要存在着特征匹配困难、鲁棒性差、系统无法自动获取初始参数和需要大量人工干预等诸多问题。利用高速CMOS摄像机与惯性传感数据融合提高了运动结构重建算法的精度及其鲁棒性。该算法在扩展卡尔曼滤波框架下是通过融合惯性与视觉传感器的数据来进行运动估计的。对场景中的每一个待估计结构的特征点建立对应的卡尔曼滤波器,以估计其空间三维结构信息。运动估计模块与结构估计模块交替运行,减小了系统运算的复杂度,提高了实时性能。通过对真实场景图像序列的实验验证结果表明,惯性传感器的额外信息能够有效地提高运动结构估计的精度,能够增强算法的鲁棒性。     

The auto-measurement of the gap of LCD glass plates using sub-pixel accuracy estimation

In this paper, the distance between laser spots has been measured using sub-pixel accuracy estimation and the accuracy has been tested in a noise environment. Here, the correlation like method (CLM) is used for the distance measurement between two laser spots and to count the estimation precision effect under noise factor by relevant CLM theorems. From the maximum value of the data series, then the position of the center of laser spot can be derived by interpolation. The estimation precision effect under noise factor and DC offset are tested and analyzed by a computer system. Also, for improving the estimation precision, a Kalman filter, which is often applied for random estimation and control theorem, is adopted. The innovation research combines CLM, and Kalman filter in sub-pixel accuracy estimation is more precise. This method can be used for measuring the gap of LCD glass plates, of optical strain gauge and the characteristics of spectral, quickly and can be automated.     

一种新的卫星钟差Kalman滤波噪声协方差估计方法

采用Kalman滤波方法进行钟差参数计算和预报时, 需确定Kalman滤波噪声协方差矩阵. 针对这一问题, 提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法, 通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型, 采用最小二乘法进行噪声参数估计. 采用精密钟差数据进行钟差参数估计和预报分析, 结果表明, 该方法具有较好的收敛性, 并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析, 验证了新方法的正确性和有效性.     

基于小波变换和维纳滤波的半导体器件1/f噪声滤波

针对半导体器件中普遍存在的1/f噪声提出了一种结合了提升小波变换和维纳滤波器的处理方法.首先利用重新加权迭代最小二乘法拟合1/f噪声的功率谱曲线得到噪声参数的估计,从而选择恰当的小波.其次,对包含了1/f噪声的信号进行提升小波变换.考虑到小波变换对1/f噪声的白化作用,利用维纳滤波器对每一层小波系数进行处理.设计了最优全通滤波器以校正维纳滤波器的相频特性,使得小波系数经滤波后相位不变.最后利用提升小波逆变换获得被1/f噪声淹没的信号.利用实验检验了提出方法的有效性,实验数据采自用于微创外科手术机器人的力传感器.结果表明提出的方法能够有效抑制1/f噪声,并使传感器的分辨力提高了25%. 关键词： 半导体器件 f噪声')" href="#">1/f噪声 提升小波变换 维纳滤波     

增量维纳滤波法在波前探测解卷积中的应用

一个常规的自适应光学系统通常包含三个重要环节：波前探测、波前校正和波前重构。因此对系统的技术要求非常高,造成系统复杂,成本昂贵。基于哈特曼-夏克波前探测的图像解卷积处理就是“事后”处理的一种,它省去了波前校正环节,使常规的自适应光学系统得到简化,降低了系统成本。其基本原理为对瞬时波面进行短时间曝光探测,同时记录相应的短时间曝光图像,来进行解卷积处理。将增量维纳滤波法应用于基于哈特曼-夏克波前探测的解卷积中,并对室内模拟点源情况下的三组畸变光斑图像数据进行了解卷积恢复处理。结果表明,将增量维纳滤波法应用于基于波前探测的解卷积是完全可行的,在室内模拟点源情况下,恢复的图像可以达到衍射极限分辨力。与维纳滤波相比,它扩展了噪声抑制因子的选取范围,在噪声抑制因子选取不正确的情况下,仍能得到比维纳滤波更好的结果。