A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain.

PURPOSE: Models based on a sum of damped exponentials occur in many applications, particularly in multicomponent T2 relaxometry. The problem of estimating the relaxation parameters and the corresponding amplitudes is known to be difficult, especially as the number of components increases. In this article, the commonly used non-negative least squares spectrum approach is compared to a recently published estimation algorithm abbreviated as Exponential Analysis via System Identification using Steiglitz-McBride. METHODS: The two algorithms are evaluated via simulation, and their performance is compared to a statistical benchmark on precision given by the Cramér-Rao bound. By applying the algorithms to an in vivo brain multi-echo spin-echo dataset, containing 32 images, estimates of the myelin water fraction are computed. RESULTS: Exponential Analysis via System Identification using Steiglitz-McBride is shown to have superior performance when applied to simulated T2 relaxation data. For the in vivo brain, Exponential Analysis via System Identification using Steiglitz-McBride gives an myelin water fraction map with a more concentrated distribution of myelin water and less noise, compared to non-negative least squares. CONCLUSION: The Exponential Analysis via System Identification using Steiglitz-McBride algorithm provides an efficient and user-parameter-free alternative to non-negative least squares for estimating the parameters of multiple relaxation components and gives a new way of estimating the spatial variations of myelin in the brain.

New approach to phase correction in multi-echo T2 relaxometry.

Estimation of the transverse relaxation time, T2, from multi-echo spin-echo images is usually performed using the magnitude of the noisy data, and a least squares (LS) approach. The noise in these magnitude images is Rice distributed, which can lead to a considerable bias in the LS-based T2 estimates. One way to avoid this bias problem is to estimate a real-valued and Gaussian distributed dataset from the complex data, rather than using the magnitude. In this paper, we propose two algorithms for phase correction which can be used to generate real-valued data suitable for LS-based parameter estimation approaches. The first is a Weighted Linear Phase Estimation algorithm, abbreviated as WELPE. This method provides an improvement over a previously published algorithm, while simplifying the estimation procedure and extending it to support multi-coil input. The algorithm fits a linearly parameterized function to the multi-echo phase-data in each voxel and, based on this estimated phase, projects the data onto the real axis. The second method is a maximum likelihood estimator of the true decaying signal magnitude, which can be efficiently implemented when the phase variation is linear in time. The performance of the algorithms is demonstrated via Monte Carlo simulations, by comparing the accuracy of the estimates. Furthermore, it is shown that using one of the proposed algorithms enables more accurate T2 estimates; in particular, phase corrected data significantly reduces the estimation bias in multi-component T2 relaxometry example, compared to when using magnitude data. WELPE is also applied to a 32-echo in vivo brain dataset, to show its practical feasibility.

Parameter estimation approach to banding artifact reduction in balanced steady-state free precession.

PURPOSE: The balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal-to-noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off-resonance effects, which we aim to minimize in this article. METHODS: We present a general and fast two-step algorithm for 1) estimating the unknowns in the bSSFP signal model from multiple phase-cycled acquisitions, and 2) reconstructing band-free images. The first step, linearization for off-resonance estimation (LORE), solves the nonlinear problem approximately by a robust linear approach. The second step applies a Gauss-Newton algorithm, initialized by LORE, to minimize the nonlinear least squares criterion. We name the full algorithm LORE-GN. RESULTS: We derive the Cramér-Rao bound, a theoretical lower bound of the variance for any unbiased estimator, and show that LORE-GN is statistically efficient. Furthermore, we show that simultaneous estimation of T1 and T2 from phase-cycled bSSFP is difficult, since the Cramér-Rao bound is high at common signal-to-noise ratio. Using simulated, phantom, and in vivo data, we illustrate the band-reduction capabilities of LORE-GN compared to other techniques, such as sum-of-squares. CONCLUSION: Using LORE-GN we can successfully minimize banding artifacts in bSSFP.

Probing waveforms and adaptive receivers for active sonar.

Active sonar systems involve the transmission and reception of one or more probing sequences, which provide a basis for extraction of target information in a region of interest. The probing sequences at the transmitter and signal processing at the receiver play crucial roles in the overall system performance. In this paper, CAN (cyclic algorithm-new) is employed to synthesize probing sequences with good aperiodic autocorrelation properties. The performance of the CAN sequences will be compared with those of pseudo random noise and random phase sequences. Two adaptive receiver designs, namely the iterative adaptive approach (IAA) and the sparse learning via iterative minimization (SLIM) method, will also be considered. IAA and SLIM will be compared with the conventional matched filter method. The performances of the algorithms will be illustrated via numerical examples, which show that CAN, IAA, and SLIM can contribute to the overall performance improvement of the active sonar systems.

A covariance fitting approach for correlated acoustic source mapping.

Microphone arrays are commonly used for noise source localization and power estimation in aeroacoustic measurements. The delay-and-sum (DAS) beamformer, which is the most widely used beamforming algorithm in practice, suffers from low resolution and high sidelobe level problems. Therefore, deconvolution approaches, such as the deconvolution approach for the mapping of acoustic sources (DAMAS), are often used for extracting the actual source powers from the contaminated DAS results. However, most deconvolution approaches assume that the sources are uncorrelated. Although deconvolution algorithms that can deal with correlated sources, such as DAMAS for correlated sources, do exist, these algorithms are computationally impractical even for small scanning grid sizes. This paper presents a covariance fitting approach for the mapping of acoustic correlated sources (MACS), which can work with uncorrelated, partially correlated or even coherent sources with a reasonably low computational complexity. MACS minimizes a quadratic cost function in a cyclic manner by making use of convex optimization and sparsity, and is guaranteed to converge at least locally. Simulations and experimental data acquired at the University of Florida Aeroacoustic Flow Facility with a 63-element logarithmic spiral microphone array in the absence of flow are used to demonstrate the performance of MACS.

Covariance-based approaches to aeroacoustic noise source analysis.

In this paper, several covariance-based approaches are proposed for aeroacoustic noise source analysis under the assumptions of a single dominant source and all observers contaminated solely by uncorrelated noise. The Cramér-Rao Bounds (CRB) of the unbiased source power estimates are also derived. The proposed methods are evaluated using both simulated data as well as data acquired from an airfoil trailing edge noise experiment in an open-jet aeroacoustic facility. The numerical examples show that the covariance-based algorithms significantly outperform an existing least-squares approach and provide accurate power estimates even under low signal-to-noise ratio (SNR) conditions. Furthermore, the mean-squared-errors (MSEs) of the so-obtained estimates are close to the corresponding CRB especially for a large number of data samples. The experimental results show that the power estimates of the proposed approaches are consistent with one another as long as the core analysis assumptions are obeyed.

Covert underwater acoustic communications.

Low probability of detection (LPD) communications are conducted at a low received signal-to-noise ratio (SNR) to deter eavesdroppers to sense the presence of the transmitted signal. Successful detection at intended receiver heavily relies on the processing gain achieved by employing the direct-sequence spread-spectrum (DSSS) technique. For scenarios that lack a sufficiently low SNR to maintain LPD, another metric, referred to as low probability of interception (LPI), is of interest to protect the privacy of the transmitted information. If covert communications take place in underwater acoustic (UWA) environments, then additional challenges are present. The time-varying nature of the UWA channel prevents the employment of a long spreading waveform. Furthermore, UWA environments are frequency-selective channels with long memory, which imposes challenges to the design of the spreading waveform. In this paper, a covert UWA communication system that adopts the DSSS technique and a coherent RAKE receiver is investigated. Emphasis is placed on the design of a spreading waveform that not only accounts for the transceiver structure and frequency-selective nature of the UWA channel, but also possesses a superior LPI. The proposed techniques are evaluated using both simulated and SPACE'08 in-water experimental data.

A robust methodology for in vivo T1 mapping.

In this article, a robust methodology for in vivo T(1) mapping is presented. The approach combines a gold standard scanning procedure with a novel fitting procedure. Fitting complex data to a five-parameter model ensures accuracy and precision of the T(1) estimation. A reduced-dimension nonlinear least squares method is proposed. This method turns the complicated multi-parameter minimization into a straightforward one-dimensional search. As the range of possible T(1) values is known, a global grid search can be used, ensuring that a global optimal solution is found. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. The performance of the new algorithm is demonstrated in simulations and phantom experiments. The new algorithm is as accurate and precise as the conventionally used Levenberg-Marquardt algorithm but much faster. This gain in speed makes the use of the five-parameter model viable. In addition, the new algorithm does not require initialization of the search parameters. Finally, the methodology is applied in vivo to conventional brain imaging and to skin imaging. T(1) values are estimated for white matter and gray matter at 1.5 T and for dermis, hypodermis, and muscle at 1.5 T, 3 T, and 7 T.

Spectral estimation of irregularly sampled exponentially decaying signals with applications to RF spectroscopy.

The problem of estimating the spectral content of exponentially decaying signals from a set of irregularly sampled data is of considerable interest in several applications, for example in various forms of radio frequency spectroscopy. In this paper, we propose a new nonparametric iterative adaptive approach that provides a solution to this estimation problem. As opposed to commonly used methods in the field, the damping coefficient, or linewidth, is explicitly modeled, which allows for an improved estimation performance. Numerical examples using both simulated data and data from NQR experiments illustrate the benefits of the proposed estimator as compared to currently available nonparametric methods.

ASEO: a method for the simultaneous estimation of single-trial event-related potentials and ongoing brain activities.

Cognitive functions are often studied by recording electric potentials from the brain over repeated presentations of a sensory stimulus or repeated performance of a motor action. Each repetition is called a trial. Recent work has demonstrated that contrary to the traditional view, the event-related potential (ERP) can vary from trial to trial and the background ongoing activity often contains rich information about the cognitive state of the brain. Based on such a variable signal plus ongoing activity model, an iterative parameter estimation method is proposed in which both the single-trial parameters of the ERP and the autoregressive representation of the ongoing activity are obtained simultaneously. This technique, referred to as the analysis of single-trial ERP and ongoing activities method, is first tested on simulation examples, and then applied to the local field potential recordings from monkeys performing a visuomotor task.

Sparsity constrained deconvolution approaches for acoustic source mapping.

Using microphone arrays for estimating source locations and strengths has become common practice in aeroacoustic applications. The classical delay-and-sum approach suffers from low resolution and high sidelobes and the resulting beamforming maps are difficult to interpret. The deconvolution approach for the mapping of acoustic sources (DAMAS) deconvolution algorithm recovers the actual source levels from the contaminated delay-and-sum results by defining an inverse problem that can be represented as a linear system of equations. In this paper, the deconvolution problem is carried onto the sparse signal representation area and a sparsity constrained deconvolution approach (SC-DAMAS) is presented for solving the DAMAS inverse problem. A sparsity preserving covariance matrix fitting approach (CMF) is also presented to overcome the drawbacks of the DAMAS inverse problem. The proposed algorithms are convex optimization problems. Our simulations show that CMF and SC-DAMAS outperform DAMAS and as the noise in the measurements increases, CMF works better than both DAMAS and SC-DAMAS. It is observed that the proposed algorithms converge faster than DAMAS. A modification to SC-DAMAS is also provided which makes it significantly faster than DAMAS and CMF. For the correlated source case, the CMF-C algorithm is proposed and compared with DAMAS-C. Improvements in performance are obtained similar to the uncorrelated case.

Multistatic adaptive microwave imaging for early breast cancer detection.

We propose a new multistatic adaptive microwave imaging (MAMI) method for early breast cancer detection. MAMI is a two-stage robust Capon beamforming (RCB) based image formation algorithm. MAMI exhibits higher resolution, lower sidelobes, and better noise and interference rejection capabilities than the existing approaches. The effectiveness of using MAMI for breast cancer detection is demonstrated via a simulated 3-D breast model and several numerical examples.

Area-selective signal parameter estimation for two-dimensional MR spectroscopy data.

We consider the problem of parametric spectral analysis of two-dimensional (2D) magnetic resonance spectroscopy (MRS) data. Estimating the signal components from 2D MRS data is becoming common practice in many clinical MR applications. The most frequently used signal processing tool for this estimation problem is the non-parametric 2D-FFT. There are several alternative parametric methods available to perform this analysis, yet their computational complexity is generally rather high and it becomes prohibitive when the number of points in the measured data matrix is large. In this paper, we propose a novel signal parameter estimation technique which operates on a pre-specified sub-area of the 2D spectrum. This area-selective approach can be used either to estimate only the signal components of main interest in the data, or to compute signal parameter estimates of all present signal components as the computational burden for each sub-area is low. In the numerical example section we consider both simulated data and in vitro 1H data acquired from a 1.5 T MR scanner.

Spectral analysis of multichannel MRS data.

The use of phased-array receive coils is a well-known technique to improve the image quality in magnetic resonance imaging studies of, e.g., the human brain. It is common to incorporate proton (1H) magnetic resonance spectroscopy (MRS) experiments in these studies to quantify key metabolites in a region of interest. Detecting metabolites in vivo is often difficult, requiring extensive scans to achieve signal-to-noise ratios (SNR) that provide suitable diagnostic results. Combining the MR absorption spectra obtained from several receive coils is one possible approach to increase the SNR. Previous literature does not give a clear overview of the wide range of possible approaches that can be used to combine MRS data from multiple detector coils. In this paper, we consider the multicoil MRS approach and introduce several signal processing tools to address the problem from different nonparametric, semiparametric, and parametric perspectives, depending on the amount of available prior knowledge about the data. We present a numerical study of these tools using both simulated 1H MRS data and experimental MRS data acquired from a 3T MR scanner.

Frequency-selective analysis of multichannel magnetic resonance spectroscopy data.

In several practical magnetic resonance spectroscopy (MRS) applications the user is interested only in the spectral content of a specific frequency band of the spectrum. A frequency-selective (or sub-band) method estimates only the parameters of those spectroscopic components that lie in a pre-selected frequency band of the spectrum in a computationally efficient manner. Multichannel MRS is a technique that employs phased-array receive coils to increase the signal-to-noise ratio (SNR) in the spectra by combining several simultaneous measurements of the magnetic resonance (MR) relaxation of an excited sample. In this paper we suggest a frequency-selective multichannel parameter estimation approach that combines the appealing features (high speed and improved SNR) of the two techniques above. The presented method shows parameter estimation accuracies comparable to those of existing fullband multichannel techniques in the high SNR case, but at a considerably lower computational complexity, and significantly better parameter estimation accuracies in low SNR scenarios.

Using prior knowledge in SVD-based parameter estimation for magnetic resonance spectroscopy--the ATP example.

We introduce the knowledge-based singular value decomposition (KNOB-SVD) method for exploiting prior knowledge in magnetic resonance (MR) spectroscopy based on the SVD of the data matrix. More specifically, we assume that the MR data are well modeled by the superposition of a given number of exponentially damped sinusoidal components and that the dampings alphakappa, frequencies omegakappa, and complex amplitudes rhokappa of some components satisfy the following relations: alphakappa = alpha (alpha = unknown), omegakappa = omega + (kappa- 1)delta (omega = unknown, delta = known), and rhokappa = Ckapparho (rho = unknown, ckappa = known real constants). The adenosine triphosphate (ATP) complex, which has one triple peak and two double peaks whose dampings, frequencies, and amplitudes may in some cases be known to satisfy the above type of relations, is used as a vehicle for describing our SVD-based method throughout the paper. By means of numerical examples, we show that our method provides more accurate parameter estimates than a commonly used general-purpose SVD-based method and a previously suggested prior knowledge-based SVD method.

Parametric methods for frequency-selective MR spectroscopy-a review.

Accurate quantitation of the spectral components in a pre-selected frequency band for magnetic resonance spectroscopy (MRS) signals is a frequently addressed problem in the MR community. One obvious application for such a frequency-selective technique is to lower the computational burden in situations when the measured data sequence contains too many samples to be processed using a standard full-spectrum method. Among the frequency-selective methods previously proposed in the literature, only a few possess the two features of primary concern: high robustness against interferences from out-of-band components and low computational complexity. In this survey paper we consider five spectral analysis methods which can be used for MRS signal parameter estimation in a selected frequency band. We re-derive the filter diagonalization method (FDM) in a new way that allows an easy comparison to the other methods presented. Then we introduce a frequency-selective version of the method of direction estimation (MODE) which has not been applied to MR-spectroscopy before. In addition, we present a filtering and decimation technique using a maximum phase bandpass FIR-filter and relate it to a similar ARMA-modeling approach known as SB-HOYWSVD (sub-band high-order Yule-Walker singular value decomposition). Finally, we study the numerical performances of these four methods and compare them to that of the recently introduced SELF-SVD (Singular Value Decomposition-based method usable in a SELected Frequency band) in several examples using simulated MR data, and discuss the benefits and disadvantages of each technique.

Wideband RELAX and wideband CLEAN for aeroacoustic imaging.

Microphone arrays can be used for acoustic source localization and characterization in wind tunnel testing. In this paper, the wideband RELAX (WB-RELAX) and the wideband CLEAN (WB-CLEAN) algorithms are presented for aeroacoustic imaging using an acoustic array. WB-RELAX is a parametric approach that can be used efficiently for point source imaging without the sidelobe problems suffered by the delay-and-sum beamforming approaches. WB-CLEAN does not have sidelobe problems either, but it behaves more like a nonparametric approach and can be used for both point source and distributed source imaging. Moreover, neither of the algorithms suffers from the severe performance degradations encountered by the adaptive beamforming methods when the number of snapshots is small and/or the sources are highly correlated or coherent with each other. A two-step optimization procedure is used to implement the WB-RELAX and WB-CLEAN algorithms efficiently. The performance of WB-RELAX and WB-CLEAN is demonstrated by applying them to measured data obtained at the NASA Langley Quiet Flow Facility using a small aperture directional array (SADA). Somewhat surprisingly, using these approaches, not only were the parameters of the dominant source accurately determined, but a highly correlated multipath of the dominant source was also discovered.

Frequency-domain method based on the singular value decomposition for frequency-selective NMR spectroscopy.

In several applications of NMR spectroscopy the user is interested only in the components lying in a small frequency band of the spectrum. A frequency selective analysis deals precisely with this kind of NMR spectroscopy: parameter estimation of only those spectroscopic components that lie in a preselected frequency band of the NMR data spectrum, with as little interference as possible from the out-of-band components and in a computationally efficient way. In this paper we introduce a frequency-domain singular value decomposition (SVD)-based method for frequency selective spectroscopy that is computationally simple, statistically accurate, and which has a firm theoretical basis. To illustrate the good performance of the proposed method we present a number of numerical examples for both simulated and in vitro NMR data.