Robust deconvolution of high-frequency ultrasound images using higher-order spectral analysis and wavelets

High resolution ultrasonic imaging of extended targets via combined match field and time delay beamforming

Ultrasound imaging using an active sensing array has been extensively studied in both time domain and frequency domain. Subspace decomposition methods in match field beamforming such as the multiple signal classification (MUSIC) algorithm can achieve subwavelength resolution of distinct point scatterers. However, when the size of the target is on the order of one wavelength or larger, the MUSIC type algorithms suffer from poor performance due to a tangled eigen structure. This paper proposes an adaptive match field beamformer that does not require subspace decomposition to achieve high resolution imaging of extended targets. Specifically, the broadband coherent white noise constraint (C-WNC) algorithm is utilized to achieve high focusing ability of extended targets by exploiting the cross-frequency coherence in an active sensing scheme. The dynamic range bias in the adaptive beamformer benefits the C-WNC algorithm to achieve high contrast regardless of the SNR. Both simulations and experiments show that the C-WNC images retain their resolution cells on the tips of the extended target with sizes ranging from a wavelength to sizes as large as the physical aperture width. A robust imaging scheme is then proposed to obtain high quality images by combining C-WNC images with a statistically stable delay-multiply-and-sum (DMAS) algorithm to create high-contrast and high-resolution images of extended targets in both azimuth and axial range directions.

Parametric study on interply tracking in multilayer composites by analytic-signal technology

Recently, researchers proposed the use of ultrasound combined with analytic-signal concepts for the reconstruction of the internal ply structure of composites. Optimal parameters for the pulse-echo mode ultrasonic testing are determined by modeling the analytic-signal response. The internal structure can be reconstructed by instantaneous metrics based on the interaction of the multilayer structure and the ultrasonic wave. However, there are certain drawbacks associated with the use of instantaneous metrics. The phase-derived interply track tends to be sensitive to the inspection conditions. This paper analytically studies the errors of the interply tracking for a wide range of parameters, including (i) signal-to-noise ratio, (ii) bandwidth, (iii) interply thickness, and (iv) attenuation, amongst others. It provides a guideline on how to improve the performance of the interply tracking procedure in real measurements. An experimental study combining the analytic-signal procedure with a standard log-Gabor filter in the frequency domain is performed to derive the interply tracks of a 24-layer composite laminate in a robust way. The bandpass filter selects the appropriate frequency band of the analytic-signal response from the composite. It shows a good ability for frequency and bandwidth selection, and can efficiently cope with noise features. The reconstructed ply tracks in A-scan, B-scan, and C-scan modes are analyzed to verify the performance of this procedure.

The Time-Domain Integration Method of Digital Subtraction Angiography Images

The clarity improvement and the noise suppression of digital subtraction angiography (DSA) images are very important. However, the common methods are very complicated. An image time-domain integration method is proposed in this study, which is based on the blood flow periodicity. In this method, the images of the first cardiac cycle after the injection of the contrast agent are integrated to obtain the time-domain integration image. This method can be used independently or as a postprocessing method of the denoising method on the signal image. The experimental results on DSA data from an aortic dissection patient show that the image time-domain integration method is efficient in image denoising and enhancement, which also has a good real-time performance. This method can also be used to improve the denoising and image enhancement effect of some common models.

Bubble-echo based deconvolution of contrast-enhanced ultrasound imaging: Simulation and experimental validations

PURPOSE

Improvement of both the imaging resolution and the contrast-to-tissue ratio (CTR) is a current emphasis of contrast-enhanced ultrasound (CEUS) for microvascular perfusion imaging. Based on the strong nonlinear characteristics of contrast agents, the CTRs have been significantly enhanced using various advanced CEUS methods. However, the imaging resolution of these methods is limited by the finite bandwidth of the imaging process. This study aimed to propose a bubble-echo based deconvolution (BED) method for CEUS with both improved resolution and CTR.

METHOD

The method is built on a modified convolution model and uses novel bubble-echo based point-spread-functions to reconstruct the images by regularized inverse Wiener filtering. Performances of the proposed BED for three CEUS modes are investigated through simulations and in vivo perfusion experiments.

RESULTS

BED of fundamental imaging was found to have the highest improvement in imaging resolution with the resolution gain up to 2.0 ± 0.2 times, which was comparable to the approved cepstrum-based deconvolution (CED). BED of second-harmonic imaging had the best performance in CTR with an enhancement of 9.8 ± 2.3 dB, which was much higher than CED. Pulse inversion BED had both a better resolution and a higher CTR.

CONCLUSION

All results indicate that BED could obtain CEUS image with both an improved resolution and a high CTR, which has important significance to microvascular perfusion evaluation in deep tissue.

Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation

The aim of this study is to solve a problem of denoising and artifact removal from in vivo multispectral photoacoustic imaging when the level of noise is not known a priori. The study analyzes Wiener filtering in Fourier domain when a family of anisotropic shape filters is considered. The unknown noise and signal power spectral densities are estimated using spectral information of images and the autoregressive of the power 1 ( AR(1)) model. Edge preservation is achieved by detecting image edges in the original and the denoised image and superimposing a weighted contribution of the two edge images to the resulting denoised image. The method is tested on multispectral photoacoustic images from simulations, a tissue-mimicking phantom, as well as in vivo imaging of the mouse, with its performance compared against that of the standard Wiener filtering in Fourier domain. The results reveal better denoising and fine details preservation capabilities of the proposed method when compared to that of the standard Wiener filtering in Fourier domain, suggesting that this could be a useful denoising technique for other multispectral photoacoustic studies.

Multi-Wiener SURE-LET deconvolution

In this paper, we propose a novel deconvolution algorithm based on the minimization of a regularized Stein's unbiased risk estimate (SURE), which is a good estimate of the mean squared error. We linearly parametrize the deconvolution process by using multiple Wiener filters as elementary functions, followed by undecimated Haar-wavelet thresholding. Due to the quadratic nature of SURE and the linear parametrization, the deconvolution problem finally boils down to solving a linear system of equations, which is very fast and exact. The linear coefficients, i.e., the solution of the linear system of equations, constitute the best approximation of the optimal processing on the Wiener-Haar-threshold basis that we consider. In addition, the proposed multi-Wiener SURE-LET approach is applicable for both periodic and symmetric boundary conditions, and can thus be used in various practical scenarios. The very competitive (both in computation time and quality) results show that the proposed algorithm, which can be interpreted as a kind of nonlinear Wiener processing, can be used as a basic tool for building more sophisticated deconvolution algorithms.

A Wavelet-Based Processing method for simultaneously determining ultrasonic velocity and material thickness

Methods of measuring ultrasonic wave velocity in an elastic sample require data on the thickness of the sample and/or the distances between the transducers and the sample. The uncertainty of the ultrasonic wave velocity measurements generally depends on that of the data available. Conversely, to determine the thickness of a material, it is necessary to have a priori information about the wave velocity. This problem is particularly hard to solve when measuring the parameters of biological specimens such as bones having a greater acoustical impedance contrast (typically 3-5 MRayl) than that of the surrounding soft tissues (typically 1.5 MRayl). Measurements of this kind cannot easily be performed. But obtaining the thickness of a bone structure and/or the ultrasonic wave velocity is a important problem, for example, in biomechanical field for the calculation of elastic modulus, or in acoustical imaging field to parameterize the images, and to reference the grey or color level set to a physical parameter. The aim of the present study was to develop a method of simultaneously and independently determining the velocity of an ultrasonic wave in an elastic sample and the wave path across the thickness of this sample, using only one acquisition in pure transmission mode. The new method, which we have called the "Wavelet-Based Processing" method, is based on the wavelet decomposition of the signals and on a suitable transmitted incident wave correlated with the experimental device, and the mathematical properties such as orthonormality, of which lend themselves well to the time-scale approach. By following an adapted algorithm, ultrasonic wave velocities in parallelepipedic plates of elastic manufactured material and the apparent thicknesses were both measured using a water tank, a mechanical device and a matched pair of 1MHz ultrasonic focused transducers having a diameter of 3mm, a focal length of 150mm and beam width of 2×2mm at the focus (mean temperature 22°). The results were compared with those obtained with a conventional Pulse-mode method and with the control values, to check their validity. Measurements performed on bovine and human dry cortical bone samples are also presented to assess the limitations of the method when it is applied to elastic biological samples, including those of an equal-wavelength size (≈1.5mm). The thicknesses and the ultrasonic wave velocities were then measured in this kind of (quasi-) parallelepipedic elastic materials with an mean estimated error ranged from 1% to 3.5% compared to the referenced values.

Estimation of average speed of sound using deconvolution of medical ultrasound data

In diagnostic ultrasound imaging the speed of sound is assumed to be 1540 m/s in soft tissues. When the actual speed is different, the mismatch can lead to distortions in the acquired images and so reduce their clinical value. Therefore, the estimation of the true speed has been pursued not only because it enables image correction but also as a way of tissue characterisation. In this article, we present a novel way to measure the average speed of sound concurrently with performing image enhancement by deconvolution. This simultaneous capability, based on a single acquisition of ultrasound data, has not been reported in previous publications. Our algorithm works by conducting non-blind deconvolution of the reflection data with point-spread functions based on different speeds of sound. Using a search strategy, we select the speed that produces the best-possible restoration. The deconvolution operates on the beamformed uncompressed radio-frequency data, without any need to modify the hardware of the ultrasound machine. A conventional handling of the transducer array is all that is required in the data acquisition part of our proposed method: the data can be collected freehand, unlike most other estimation methods. We have tested our algorithm with simulations, in vitro phantoms with known and unknown speeds and in vivo scans. The estimation error was found to be +0.19 +/- 8.90 m/s (mean +/- standard deviation) for in vitro in-house phantoms whose speeds were also measured independently. In addition to the speed estimation, our method has also proved to be capable of simultaneously producing a better restoration of ultrasound images than deconvolution by an assumed speed of 1540 m/s, when this assumption is incorrect.

Two-dimensional blind Bayesian deconvolution of medical ultrasound images

A new approach to 2-D blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radio-frequency image data are modeled as a convolution of the point-spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point-spread function, and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator to further stabilize the process. The proposed method is an extension of the homomorphic deconvolution, which is used here only to compute the initial estimate of the point-spread function. Homomorphic deconvolution is based on the assumption that the point-spread function and the tissue function lie in different bands of the cepstrum domain, which is not completely true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally to the complete radiofrequency image data. Thus, only the global part of the point-spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.

Ultrasonic imaging using a computed point spread function

An explicit point spread function (PSF) evaluator in the frequency domain is described for an ultrasonic transducer operating in the pulse-echo mode. The PSF evaluator employs the patch element model for transducer field determination and scattered field assessment from a small but finite "point" reflector. The PSF for a planar transducer in a medium has been evaluated in the near and the far field. The computed PSFs were used to deconvolve and restore surface images, obtained experimentally, of a single hole and a five-hole cluster in an Al calibration block. A calibration plot is arrived at for estimating, without the need for deconvolution, the actual diameters of circular reflectors from apparent diameters obtained experimentally for a single-medium imaging configuration. The PSF, when the transducer and the point reflector are in two media separated by a planar interface, was evaluated in the near and far field. The computed PSFs were used to deconvolve and restore subsurface images, obtained experimentally, of flat bottom holes (FBHs) in an Al calibration block. We show that the PSF, in the presence of a planar interface, can be obtained from a single-medium PSF model using an effective single-medium path length concept. The PSFs and modulation transfer functions (MTFs) are evaluated for spherical focused and annular transducers and compared with those for the planar transducer. We identify imaging distances to get better-resolved images when using planar, spherical focused, and annular transducers.

Wavelet restoration of medical pulse-echo ultrasound images in an EM framework

The clinical utility of pulse-echo ultrasound images is severely limited by inherent poor resolution that impacts negatively on their diagnostic potential. Research into the enhancement of image quality has mostly been concentrated in the areas of blind image restoration and speckle removal, with little regard for accurate modeling of the underlying tissue reflectivity that is imaged. The acoustic response of soft biological tissues has statistics that differ substantially from the natural images considered in mainstream image processing: although, on a macroscopic scale, the overall tissue echogenicity does behave some-what like a natural image and varies piecewise-smoothly, on a microscopic scale, the tissue reflectivity exhibits a pseudo-random texture (manifested in the amplitude image as speckle) due to the dense concentrations of small, weakly scattering particles. Recognizing that this pseudorandom texture is diagnostically important for tissue identification, we propose modeling tissue reflectivity as the product of a piecewise-smooth echogenicity map and a field of uncorrelated, identically distributed random variables. We demonstrate how this model of tissue reflectivity can be exploited in an expectation-maximization (EM) algorithm that simultaneously solves the image restoration problem and the speckle removal problem by iteratively alternating between Wiener filtering (to solve for the tissue reflectivity) and wavelet-based denoising (to solve for the echogenicity map). Our simulation and in vitro results indicate that our EM algorithm is capable of producing restored images that have better image quality and greater fidelity to the true tissue reflectivity than other restoration techniques based on simpler regularizing constraints.

Constrained least squares filtering algorithm for ultrasound image deconvolution

A new medical ultrasound tissue model is considered in this paper, which incorporates random fluctuations of the tissue response and provides more realistic interpretation of the received pulse-echo ultrasound signal. Using this new model, we propose an algorithm for restoration of the degraded ultrasound image. The proposed deconvolution is a modification of the classical regularization technique which combines Wiener filter and the constrained least squares (LS) algorithm for restoration of the ultrasound image. The performance of the algorithm is evaluated based on both the simulated phantom images and real ultrasound radio frequency (RF) data. The results show that the algorithm can provide improved ultrasound imaging performance in terms of the resolution gain. The deconvolved images visually show better resolved tissue structures and reduce speckle, which are confirmed by a medical expert.