Correlative averaging for radial magnetic resonance imaging

Although Magnetic Resonance Imaging (MRI) has faced a dramatic increase in real-time capabilities over the last year, acceptable image quality still limits the actually achievable acquisition speed. This paper presents a motion-compensated noise filter that, on the basis of hierarchical motion estimation and edge-preserving adaptive weighted averaging, has been integrated into a segmented radial MR acquisition scheme. In several studies of moving joints, the proposed approach led to significant reductions in the noise level without introducing motion blur. The improved image quality would, in principle, allow more than double the acquisition speed, retaining the original image quality.     

Anisotropic 2-D and 3-D averaging of fMRI signals

A novel method for denoising functional magnetic resonance imaging temporal signals is presented in this note. The method is based on progressively enhancing the temporal signal by means of adaptive anisotropic spatial averaging. This average is based on a new metric for comparing temporal signals corresponding to active fMRI regions. Examples are presented both for simulated and real two and three-dimensional data. The software implementing the proposed technique is publicly available for the research community.     

Maximum-likelihood bearing estimation with partly calibrated arraysin spatially correlated noise fields

The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions. More exactly, this paper assumes that the array contains some calibrated sensors, whose number is only required to be larger than the number of signals impinging on the array, and also that the noise in the calibrated sensors is uncorrelated with the noise in the other sensors. These two noise vectors, however, may have arbitrary spatial autocovariance matrices. Under these assumptions the many nuisance parameters (viz., the elements of the signal and noise covariance matrices and the transfer and location characteristics of the uncalibrated sensors) can be eliminated from the likelihood function, leaving a significantly simplified concentrated likelihood whose maximum yields the ML bearing estimates. The ML estimator introduced in this paper, and referred to as MLE, is shown to be asymptotically equivalent to a recently proposed subspace-based bearing estimator called UNCLE and rederived herein by a much simpler approach than in the original work. A statistical analysis derives the asymptotic distribution of the MLE and UNCLE estimates, and proves that they are asymptotically equivalent and statistically efficient. In a simulation study, the MLE and UNCLE methods are found to possess very similar finite-sample properties as well. As UNCLE is computationally more efficient, it may be the preferred technique in a given application     

Information theoretic criteria for the determination of the numberof signals in spatially correlated noise

The problem of determining the number of signals in high-resolution array processing when the noise is spatially correlated (having an unknown covariance matrix) is examined. By considering a model in which two sensor arrays are well separated such that their noise outputs are uncorrelated, the authors develop a likelihood function whose maximum can be expressed in a very simple form involving the canonical correlation coefficients. This likelihood function and a choice of penalty functions constitute a number of new information theoretic criteria suitable for the determination of the number of signals in an unknown correlated noise environment. Furthermore, it is demonstrated that the new criteria are applicable in the case when only one sensor array is available     

Space-time fading channel estimation and symbol detection inunknown spatially correlated noise

We present maximum likelihood (ML) methods for space-time fading channel estimation with an antenna array in spatially correlated noise having unknown covariance; the results are applied to symbol detection. The received signal is modeled as a linear combination of multipath-delayed and Doppler-shifted copies of the transmitted waveform. We consider structured and unstructured array response models and derive the Cramer-Rao bound (CRB) for the unknown directions of arrival, time delays, and Doppler shifts. We also develop methods for spatial and temporal interference suppression. Finally, we propose coherent matched-filter and concentrated-likelihood receivers that account for the spatial noise covariance and analyze their performance     

An auto-calibration method for spatially and temporally correlated noncircular sources in unknown noise fields

Angularly dependent gain and phase uncertainties are produced by the combined effects of multiple sensor errors. This paper proposes a direction-finding method for noncircular signals in the presence of angularly dependent gain/phase errors, which utilizes instrumental sensors to achieve auto-calibration and relies on an improved alternating projection procedure. By applying the principle of the extended 2-sided instrumental variable signal subspace fitting algorithm, the proposed method is effective for separating spatially and temporally correlated noncircular sources from the unknown colored (i.e., spatially correlated) noise. Considering that modeling errors of instrumental sensors are frequently encountered in practice, this paper also presents a theoretical derivation for the closed-form expression of the mean square error of the estimation under the influence of modeling errors of instrumental sensors in the first-order analysis. Finally, the results of two series of simulations are demonstrated. The first series of simulations verifies the effectiveness of the proposed auto-calibration method, and shows that noncircularity and temporal correlation of sources are informative for enhancing the calibration performance of our method. The results also prove that the proposed method performs better than the instrumental sensor method when applied to spatially and temporally correlated noncircular sources. Moreover, this performance advantage of our method is more prominent when signal-to-noise ratio is low, or in spatially correlated noise fields. The second series of simulations validates the theoretical prediction, and thus our statistical analysis has a high predictive value for calibration performance of the proposed method under the influence of modeling errors.     

SNR enhancement in radial SSFP imaging using partial k-space averaging

The steady-state free precessing (SSFP) sequences, widely used in MRI today, acquire data only during a short fraction of the repetition time (TR). Thus, they exhibit a poor scan efficiency. In this paper, a novel approach to extending the acquisition window for a given TR without considerably modifying the basic sequence is explored for radial SSFP sequences. The additional data are primarily employed to increase the signal-to-noise ratio, rather than to improve the temporal resolution of the imaging. The approach is analyzed regarding its effect on the image SNR (signal to noise ratio) and the reconstruction algorithm. Results are presented for phantom experiments and cardiac functions studies. The gain in SNR is most notable in rapid imaging, since SNR enhancement for a constant repetition time may be used to compensate for the increase in noise resulting from angular undersampling.     

2-D VCSEL model for investigation of dynamic fiber coupling and spatially filtered noise

We propose a set of transformed two-dimensional (2-D) rate equations, which allow the computation of dynamic gain competition resulting from inhomogeneous field and carrier spatial distributions inside a vertical cavity surface-emitting laser cavity. Any explicit spatial dependency has been removed from the modified equations, reducing the computational time by several orders of magnitude. Resulting 2-D dynamic intensity profiles allow investigating effects related to improper fiber coupling due to transverse misalignment between laser beam and fiber. Although the expected increased relative intensity noise (RIN) levels associated with mode partition noise are observed, other effects might have larger contributions to the total noise under specific conditions. We show that the minimum RIN level is not necessarily reached for zero misalignment, but at positions where modes with broad far-field profiles and low power experience important filtering.     

A phase noise mitigation scheme for MIMO WLANs with spatially correlated and imperfectly estimated channels

Applying multiple input multiple output (MIMO) technique to OFDM-based wireless local area networks (WLANs) promises impressive high capacity and spectral efficiency compared with conventional systems. However, similar to SISO-OFDM, MIMO-OFDM suffers significant performance degradation due to the presence of phase noise. Many methods have been developed to mitigate phase noise for a single antenna system with perfect channel estimation, whereas none has been proposed for correlated MIMO-OFDM scenarios. Therefore, in this letter, by using the phase noise correlation function, a new phase noise mitigation scheme is proposed for the general M/sub T//spl times/M/sub R/ MIMO WLANs system with channel estimation errors. Numerical results show that, compared with conventional approaches, the proposed scheme achieves significant performance gain with high spectral efficiency, requiring few pilots, and is robust to spatial correlation and channel estimation errors, which makes it very attractive for practical applications.     

EM‐based iterative receiver for coded MIMO systems in unknown spatially correlated noise

We present iterative channel estimation and decoding schemes for multi‐input multi‐output (MIMO) Rayleigh block fading channels in spatially correlated noise. An expectation‐maximization (EM) algorithm is utilized to find the maximum likelihood (ML) estimates of the channel and spatial noise covariance matrices, and to compute soft information of coded symbols which is sent to an error‐control decoder. The extrinsic information produced by the decoder is then used to refine channel estimation. Several iterations are performed between the above channel estimation and decoding steps. We derive modified Cramer–Rao Bound (MCRB) for the unknown channel and noise parameters, and show that the proposed EM‐based channel estimation scheme achieves the MCRB at medium and high SNRs. For a bit error rate of 10⁻⁶ and long frame length, there is negligible performance difference between the proposed scheme and the ideal coherent detector that utilizes the true channel and noise covariance matrices. Copyright © 2006 John Wiley & Sons, Ltd.     

Depth-buffer targeting for spatially accurate 3-D visualization of medical images

During interactive image-guided surgery (IIGS), a surgeon uses data from medical images to help guide the surgical procedure. At Vanderbilt University, an IIGS software system called Orion has been developed which is capable of displaying up to four 512 x 512 images and the current surgical position using an active optical tracking system. Orion is capable of displaying data from any tomographic image volume and from any NTSC video image. An additional display module has been implemented to display three-dimensional information as well as the tomographic slices. This provides the surgeon with valuable anatomical information that is not readily obtained from the tomographic slices alone. Before the surgery, a set of rendered images is created, each with a different angular view of the tomographic volume in order to surround the site of surgical interest. The major objectives of the display module are to display the appropriate rendered image from the set, identify the current probe position on the selected image, and provide an indication of distance between the probe and the physical point of the anatomy indicated on the image. This can provide the surgeon with vital information such as distance to blood vessels, tumors, or other critical structures.     

一种空间相关高斯噪声背景下的时变时延估计算法

在空间相关高斯噪声的背景下,基于二阶统计量的时延估计方法会失效,该文提出了一种基于三阶统计量的自适应时变时延估计算法,并分析了算法的收敛性,最后的仿真结果表明该算法可以有效地抑制相关高斯噪声。     

Total least squares phased averaging and 3-D ESPRIT for jointazimuth-elevation-carrier estimation

The method of total least squares (TLS) phased averaging for high-performance subspace fitting in the three-dimensional (3-D) case of spectral estimation with 3-D ESPRIT is introduced and applied to the joint azimuth elevation-carrier estimation problem with two-dimensional (2-D) uniform rectangular arrays. The method is highly efficient computationally and is suitable for large arrays. Detailed computer experiments and comparisons are provided. For a 16×16 array of sensors and heavy noise, TLS phased-averaging 3-D ESPRIT exceeds the 3-D TLS unitary ESPRIT estimator by 300% in RMSE performance     

Asymptotic performance analysis of information-theoretic criteriafor determining the number of signals in spatially correlated noise

A new information-theoretic criterion for the determination of the number of signals in unknown correlated noise was derived in Zhang and Wong (1993) supported with computer simulations. In the present correspondence, the theoretic performance of the criterion is presented. The authors establish a simple expression, in a closed form, for the probability of correct estimation by the criterion. Then on this basis, they analyze its consistency properties, showing that the criterion is consistent if the penalty coefficient α=(1/2)logN and inconsistent if α=1     

Vibrating varifocal mirrors for 3-D imaging

As technology advances, the interactions between man and machine become more complex. A reliable three-dimensional man-machine interface could help alleviate some of the complexity; but an effective 3-D display has, so far, eluded technologists. Thus, we have been forced to make unnatural compromises when dealing with data that are essentially three-dimensional in nature. This article describes a system that may meet the autostereoscopic display requirements in many situations.     

空间相关MIMO信道下的有限反馈预编码

对于独立同分布的瑞利衰落信道,Grassmann码本可以取得良好的系统性能,但是当MIMO信道存在空间相关性时,该码本不可避免的带来性能损失,本文针对空间相关的MIMO信道,通过用发送相关矩阵的平方根对传统Grassmann码本进行旋转,然后再量化,得到适用于空间相关信道下的新码本,并通过实验仿真阐释了新得到的码本对于传统码本在误码率和信道容量方面等方面的性能优势。     

Efficient 2D DOA estimation of coherent signals in spatially correlated noise using electromagnetic vector sensors

This paper considers a new azimuth-elevation DOA estimation algorithm for multiple signals using electromagnetic vector sensor array. We firstly exploit the planar-plus-an-isolated sensor array geometry (Li et al. in IEE Proc Radar Sonar Navig 143(5):295–299, 1996) to define a full rank cross-covariance matrix. Then we develop an efficient ESPRIT-like algorithm using the so-called propagator to estimate the steering vectors of electromagnetic vector sensor, without performing eigen-decomposition into signal subspaces. Finally, we compute the vector cross product to obtain the closed-form azimuth-elevation angle estimates. The new algorithm does not require 2D iterative searching, and is applicable to coherent (fully correlated) signals and spatially correlated noise. In addition, the proposed algorithm offers enhanced estimation precision by sparse array aperture extension, but suffers no DOA cyclical ambiguity. Monte-Carlo simulations are presented to verify the effectiveness of the proposed algorithm.     

Ray phase space approach for 3-D imaging and 3-D optical data representation

We propose a framework to model, analyze and design three-dimensional (3-D) imaging systems. A system engineering approach is adopted which relates 3-D images (real or synthesized) to 3-D objects (real or synthesized) using a novel representation of the optical data which we call "ray phase space". The framework provides a powerful tool for determining the performance of 3-D imaging systems, for generating computational reconstruction of 3-D images and for optimizing 3-D imaging systems.     

Coherent 3-D echo detection for ultrasonic imaging

The purpose of the present paper is to present an ultrasonic processing set-up by which three-dimensional (3-D) echo location can be computed more efficiently than by other one-dimensional (1-D) methods. This set-up contains three successive tasks. The first one deals with a model for representing echoes. This model is based on a generic wavelet, which is a cosine function with variable amplitude and phase. To estimate the wavelet, we propose to use a spline representation of its complex envelope in order to reduce amplitude and phase dimension. The second task deals with 1-D detection and is conducted within a Bayesian framework. Using an Ascan decomposition on a family of wavelets resulting from the first task, we propose a specific procedure to carry on constrained least-squares in order to alleviate the bias inherent to this criterion. The third task deals with the spatial regularization of the detected echo location field resulting from the second task. We propose a Bayes-Markov model for removing isolated wrong detections and simultaneously improving, under regularization constraint, the spatial location of the detected echoes. In fact, this model deals with the general problem of nonorganized point approximation. All the proposed techniques are illustrated on real ultrasonic data.     

On noncoherent receivers for DSTM in spatially correlated fading

In this letter, we derive a multiple-symbol differential detection (MSDD) and a novel MSDD-based decision-feedback differential detection (MS-DFDD) receiver for differential space-time modulation transmitted over spatially correlated multiple-input multiple-output fading channels. We show that MS-DFDD outperforms previously proposed DFDD schemes that are based on scalar and vector prediction (SP-DFDD and VP-DFDD). In addition, we prove that at high signal-to-noise ratio (SNR) VP-DFDD is equivalent to SP-DFDD and thus fails to properly exploit the spatial fading correlations.