Estimation of kinetic parameters without input functions: analysis of three methods for multichannel blind identification

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by multichannel blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. Three blind identification algorithms are analyzed here to assess their utility in medical imaging: eigenvector-based algorithm for multichannel blind deconvolution; cross relations; and iterative quadratic maximum-likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. Tissue responses corresponding to a physiological two-compartment model are primarily considered. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that IQML gives more accurate estimates than the other two blind identification methods.     

基于卡尔曼滤波的同步盲系统辨识与解卷积方法

在盲信道均衡或盲语音去混响应用中,盲多信道系统辨识通常是信号解卷积的前提条件,即盲辨识过程后跟一个解卷积过程。本文提出一种基于卡尔曼滤波的同步盲系统辨识与解卷积方法,其中卡尔曼滤波的状态矢量由多信道系统参数与源信号矢量组成,过程方程和测量方程则建立在单输入-多输出系统(SIMO)的输入输出关系及信道间交叉关联关系(Cross Relation)基础上。此外,盲系统辨识部分与解卷积部分是可以解耦的,生成两个看似独立的卡尔曼滤波问题,并且这两个卡尔曼滤波问题可以实现并行计算。与级联结构相比,这种并行结构更有利于算法优化和实时信号处理。仿真表明,对于无噪声理想信号模型,本算法可以实现完全系统辨识和解卷积(信号误差比可达到100 dB以上),说明理论正确;对于实测的混响语音信号亦可以实现一定的去混响效果。      

Gradient Adaptive Paraunitary Filter Banks for Spatio-Temporal Subspace Analysis and Multichannel Blind Deconvolution

Paraunitary filter banks are important for several signal processing tasks, including coding, multichannel deconvolution and equalization, adaptive beamforming, and subspace processing. In this paper, we consider the task of adapting the impulse response of a multichannel paraunitary filter bank via gradient ascent or descent on a chosen cost function. Our methods are spatio-temporal generalizations of gradient techniques on the Grassmann and Stiefel manifolds, and we prove that they inherently maintain the paraunitariness of the multichannel adaptive system over time. We then discuss the necessary practical approximations, modifications, and simplifications of the methods for solving two relevant signal processing tasks: (i) spatio-temporal subspace analysis and (ii) multichannel blind deconvolution. Simulations indicate that our methods can provide simple, useful solutions to these important problems.     

Time delay for arrival of MR contrast agent in collateral-dependent myocardium

An analysis of the kinetics of myocardial contrast enhancement is an important component of myocardial perfusion studies. The contrast enhancement can be modeled by a linear time-invariant system, and the myocardial impulse response, calculated by deconvolution of the measured tissue response with an arterial input, gives a direct estimate of myocardial blood flow. In this paper, we analyze the effects of delays in the contrast enhancement, that occur in collateral-dependent myocardium, where the tracer reaches the tissue region only through branches from other coronary arteries that form natural bypass vessels. We investigate how the delayed arrival of tracer alters the myocardial impulse response. Model-independent deconvolution is applied to determine the lag between arterial input and tissue enhancement. Experimental data in a porcine model of collateral development indicate that the delayed arrival of an injected tracer, measured at rest, is a useful marker to identify collateral-dependent myocardium, and predict its flow capacitance.     

基于聚谱分析的多通道盲信号自适应分离算法

该文提出一种在多传感器中多种信号混叠的分离方法。该方法通过分析传感器数据的聚谱来提取未知信号,并利用线性方程基本算法估计有限脉冲响应的耦合系统,该方法对于多通道谱重叠的有色输入信号盲解卷积十分有效。作为该算法的扩展,可以应用于包括准周期信号等非平稳信号的分离。并将该算法应用于电磁辐射的测试,仿真结果证明了其有效性和快速性。     

具有约束学习速率的频域盲均衡算法

多通道的盲均衡在语音分离、去混响、通信、信号处理和控制等领域具有广泛的应用,本文基于二阶统计量在频域重新构造评价函数,为避免得到平凡解在评价函数中额外引入了一项不可简约多项式的描述.然后,应用自然梯度法导出盲均衡算法,为了确保算法收敛,同时给出迭代收敛条件,并将它用来约束学习速率.仿真实验表明,该算法能够分离可均衡的有限冲激响应系统,从而证实了该算法的有效性.     

Super-exponential blind adaptive beamforming

The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources. In this paper, we present a higher order cumulant-based beamforming algorithm, namely, the super-exponential blind adaptive beamforming algorithm, which is extended from the super-exponential algorithm (SEA) and the inverse filter criteria (IFC). While both SEA and IFC assume noise-free conditions, this requirement is no longer needed, and all the noise components are taken into account in the proposed algorithm. Two special conditions are derived under which the proposed blind beamforming algorithm achieves the performance of the corresponding optimal nonblind beamformer in the sense of minimum mean square error (MMSE). Simulation results show that the proposed algorithm is effective and robust to diverse initial weight vectors; its performance with the use of the fourth-order cumulants is close to that of the nonblind optimal MMSE beamformer.     

气动光学效应图像恢复IBD算法研究

采用迭代盲目反卷积进行气动光学效应图像恢复研究,编制了相应的计算程序,获得了恢复图像和相应的气动光学效应降质过程的点扩散函数,同时讨论了共轭梯度CG算法在盲目反卷积图像恢复计算过程中的收敛性,并提出算法策略。     

Iterative multichannel blind deconvolution method for temporally colored sources

An iterative separation approach, i.e. source signals are extracted and removed one by one, is proposed for multichannel blind deconvolution of colored signals. Each source signal is extracted in two stages: a filtered version of the source signal is first obtained by solving the generalized eigenvalue problem, which is then followed by a single channel blind deconvolution based on ensemble learning. Simulation demonstrates the capability of the approach to perform efficient mutichannel blind deconvolution.     

Blind deconvolution of medical ultrasound images: a parametric inverse filtering approach.

The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a "hybridization" of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the "hybrid" approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolutioh algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.     

Frequency-domain blind deconvolution based on mutual information rate

In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian source) and allows non-minimum-phase filter estimation. In practice, the criterion contains a regularization term for limiting noise amplification as in Wiener filtering. The score function estimation, which represents a key point of the algorithm, is detailed, and the most robust estimate is selected. Finally, experiments point to the relevance of the proposed algorithm: 1) any filter, minimum phase or not, can be estimated and 2) on actual data (underwater explosions, seismovolcanic phenomena), this deconvolution algorithm provides good results with a better tradeoff between deconvolution quality and noise amplification than existing methods.     

Total variation blind deconvolution employing split Bregman iteration

Blind image deconvolution is one of the most challenging problems in image processing. The total variation (TV) regularization approach can effectively recover edges of image. In this paper, we propose a new TV blind deconvolution algorithm by employing split Bregman iteration (called as TV-BDSB). Considering the operator splitting and penalty techniques, we present also a new splitting objective function. Then, we propose an extended split Bregman iteration to address the minimizing problems, the latent image and the blur kernel are estimated alternately. The TV-BDSB algorithm can greatly reduce the computational cost and improve remarkably the image quality. Experiments are conducted on both synthetic and real-life degradations. Comparisons are also made with some existing blind deconvolution methods. Experimental results indicate the advantages of the proposed algorithm.     

Optimal Signaling and Selection Verification for Single Transmit-Antenna Selection

In marked contrast with the ideal error-free feedback assumption that is common in the literature, practical systems are likely to have severely bandwidth-limited, error-prone feedback channels. We consider the scenario where feedback from the receiver is used by the transmitter to select the best antenna, out of many available antennas, for data transmission. Feedback errors cause the transmitter to select an antenna different from the one signaled by the receiver. We show that optimizing the signaling assignment, which maps the antenna indices to the feedback codewords, improves performance without introducing any additional redundancy. For a system that uses error-prone feedback to transmit quadrature-phase-shift-keying-modulated data from a single antenna selected from many available spatially correlated antennas, we derive closed-form approximations for the data symbol error probability for an arbitrary number of receive antennas. We use these to systematically find the optimal signaling assignments using a low-complexity algorithm. The optimal signaling is intimately coupled to how the receiver performs selection verification, i.e., how it decodes the data signal when, due to feedback errors, it does not always know which antenna was used for data transmission. We show that ignoring feedback errors at the receiver can lead to an unacceptable performance degradation, and develop optimal and suboptimal, blind and nonblind selection-verification methods. With a small side-information overhead, nonblind verification approaches the ideal perfect selection-verification performance     

一种无源双基地雷达直达波恢复方法研究

在山地及城市附近的应用中,严重的多径杂波造成基于调频广播的无源双基地雷达直达波信号恢复困难。文中提出了采用基于空间分集的常数模盲均衡算法,利用空间分集的多通道,实现信号衰落的补偿,而利用常数模盲均衡算法,实现恒模的调频广播信号的反卷积运算,从而完成了对多径的抑制,获得高质量的直达波信号。计算机仿真表明：与无分集的常数模盲均衡算法相比,所提算法获得了更好的目标检测性能。     

Relationships between the constant modulus and Wiener receivers

The Godard (1980) or the constant modulus algorithm (CMA) is an effective technique for blind receiver design in communications. However, due to the complexity of the constant modulus (CM) cost function, the performance of the CM receivers has primarily been evaluated using simulations. Theoretical analysis is typically based on either the noiseless case or approximations of the cost function. The following question, while resolvable numerically for a specific example, remains unanswered in a generic manner. In the presence of channel noise, where are the CM local minima and what are their mean-squared errors (MSE)? In this paper, a geometrical approach is presented that relates the CM to Wiener (or minimum MSE) receivers. Given the MSE and the intersymbol/user interference of a Wiener receiver, a sufficient condition is given for the existence of a CM local minimum in the neighborhood of the Wiener receiver. The MSE bounds on CM receiver performance are derived and shown to be tight in simulations. The analysis shows that, while in some cases the CM receiver performs almost as well as the (nonblind) Wiener receiver, it is also possible that, due to its blind nature, the CM receiver may perform considerably worse than a (nonblind) Wiener receiver     

Blind channel estimation and data detection using hidden Markovmodels

We propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum-Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for time-varying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver     

Blind deconvolution of ultrasound sequences using nonparametric local polynomial estimates of the pulse

The problem of reconstructing the reflectivity of a biological tissue is examined by means of blind deconvolution of the echo ultrasound signals. It is shown that the quality of the reconstruction procedure can be significantly improved when initially the ultrasonic pulse is accurately estimated. A new approach to the estimation of the ultrasound pulse echo sequences is proposed, using local polynomial approximation, which is closely related to the wavelet transform theory. This approach can be viewed as a modification of homomorphic deconvolution, by using bases different from the Fourier basis of the space of square-integrable functions L2. The bases used here are the orthogonal compactly supported wavelet bases. It is shown that the locality of the estimate can be extremely useful in number of cases of practical interest, resulting in estimates with smaller root-mean squared (rms) errors, as compared with estimates employing the Fourier basis. This approach is applied to ultrasound signals, for estimation of the ultrasound pulse log-spectrum from the log-spectrum of radio-frequency (RF) sequences. It is shown, conceptually and experimentally, that the proposed approach can provide robust and rapidly computed estimates of the ultrasound pulses from the RF-sequences, as obtained in the process of tissue scanning. The pulse phase was recovered using the minimum-phase assumption, which was found to hold for the transducers in use. The obtained pulse estimates are used for the deconvolution of the RF-sequences, which result in stable estimates of the tissue reflectivity function, fairly independent of the properties of the imaging system. Simulated data, data obtained from several phantoms and from in vitro experiments have been processed and the results seem to be quite promising.     

基于不完整约束自然梯度算法的多道盲解卷

针对多输入多输出(MIMO)系统的Bussgang算法可能收敛到错误的解，而且收敛速度慢的缺点。章提出了不完整约束的自然梯度算法，该算法是由不完整约束条件与自然梯度算法的结合而推导出来的。通过计算机仿真对这两种多道盲解卷算法进行了比较，仿真试验表明：提出的算法收敛速度快，并且比Bussgang算法稳定。     

Interference rejection using the time-dependent constant modulusalgorithm (CMA) and the hybrid CMA/spectral correlation discriminator

Two new blind adaptive filtering algorithms for interference rejection using time-dependent filtering structures are presented. The time-dependent structure allows the adaptive filter to outperform the conventional adaptive filter implemented with a time-independent structure for filtering of cyclostationary communication signals. At the same time, the blind adaption algorithms allow the filters to operate without the use of an external training signal. The first algorithm applies the CMA to an unconstrained time-dependent filtering structure. The second algorithm applies the CMA to a spectral correlation discriminator, which is constrained to select signals with unique spectral correlation characteristics. Using computer simulations, it is shown that the blind time-dependent filtering algorithms can provide mean-square errors (MSEs) and bit error rates (BERs) that are significantly lower than the MSEs and BERs provided using conventional time-independent adaptive filters. It is also shown that these processors can outperform the nonblind training-sequence directed time-independent adaptive filter     

基于非参数密度估计的多通道盲解卷积

非参数密度估计方法被用来直接估计在自然梯度盲解郑积算法中遇到的评价函数(score function)。与用一个非线性函数简单地代替评价函数相比较,这种直接估计评价函数的方法的主要优点是:它可以用来对杂系混合信号,即同时包含超高斯和亚高斯的信号,进行盲解卷积。因为评价函数可以被直接的估计出来,因此,就不需要针对不同的源信号选择不同的非线性函数来代替评价函数。这种方法可以用在更加“盲”的情况。