Two-dimensional noise-robust blind deconvolution of ultrasound images

Ultrasound technology assisted colloidal nanocrystal synthesis and biomedical applications

Non-invasive and high spatiotemporal resolution mythologies for the diagnosis and treatment of disease in clinical medicine promote the development of modern medicine. Ultrasound (US) technology provides a non-invasive, real-time, and cost-effective clinical imaging modality, which plays a significant role in chemical synthesis and clinical translation, especially in in vivo imaging and cancer therapy. On the one hand, the US treatment is usually accompanied by cavitation, leading to high temperature and pressure, so-called "hot spot", playing a significant role in sonochemical-based colloidal synthesis. Compared with the classical nucleation synthetic method, the sonochemical synthesis strategy presents high efficiency for the fabrication of colloidal nanocrystals due to its fast nucleation and growth procedure. On the other hand, the US is attractive for in vivo and medical treatment, with applications increasing with the development of novel contrast agents, such as the micro and nano bubbles, which are widely used in neuromodulation, with which the US can breach the blood-brain barrier temporarily and safely, opening a new door to neuromodulation and therapy. In terms of cancer treatment, sonodynamic therapy and US-assisted synergetic therapy show great effects against cancer and sonodynamic immunotherapy present unparalleled potentiality compared with other synergetic therapies. Further development of ultrasound technology can revolutionize both chemical synthesis and clinical translation by improving efficiency, precision, and accessibility while reducing environmental impact and enhancing patient care. In this paper, we review the US-assisted sonochemical synthesis and biological applications, to promote the next generation US technology-assisted applications.

Deconvolution based on sparsity and continuity improves the quality of ultrasound image

The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

Improved ultrasound image quality with pixel-based beamforming using a Wiener-filter and a SNR-dependent coherence factor

Pixel-based beamforming generates focused data by assuming that the waveforms received on a linear transducer array are composed of spherical pulses. It does not take into account the spatiotemporal spread in the data from the length of the excitation pulse or from the transfer functions of the transducer elements. As a result, these beamformers primarily have impacts on lateral, rather than axial, resolution. This paper proposes an efficient method to improve the axial resolution for pixel-based beamforming. We extend our field pattern analysis and show that the received waveforms should be passed through a Wiener filter before being used in the coherent pixel-based beamformer. This filter is designed based on signals echoed from a single scatterer at the transmit focus. The beamformer output is then combined with a coherence factor, that is adaptive to the signal-to-noise ratio, to improve the image contrast and suppress artifacts that have arisen during the filtering process. We validate the proposed method and compare it with other beamforming strategies using a series of experiments, including simulation, phantom and in vivo studies. It is shown to offer significant improvements in axial resolution and contrast over coherent pixel-based beamforming, as well as other spatial filters derived from synthetic aperture imaging. The method also demonstrates robustness to modeling errors in the experimental data. Overall, the imaging results show that the proposed approach has the potential to be of value in clinical applications.

Motion blur invariant for estimating motion parameters of medical ultrasound images

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

Super-resolution acoustic image montage via a biaxial metamaterial lens

Ever since the Victorian era, montage, the process of pictorial composition made by juxtaposing or superimposing photographs, has been a very popular post-editing imaging technique. Despite showing a strong power in demonstrating complex wave field effects, this technique has neither been fully explored in acoustic imaging nor been realized in real-time systems with the capability beyond diffraction limits. On the other hand, the recent prospect of metamaterials has shown their great potentials in super-resolution acoustic imaging. However, the miracle jigsaw of more advanced functional modulation of acoustic wave fields at deep subwavelength scale still remains elusive. Here we report the experimental implementation of super-resolution acoustic image montage through a judiciously designed biaxial metamaterial lens. Based on the non-diffraction birefringence in the biaxial metamaterials, we realized various montage functionalities such as duplication, composition, and decomposition of sound images with distinctive deep subwavelength features. Our work represents an important step in developing versatile functional acoustic metamaterial devices for imaging purposes, as it provides on-demand editing of sound field patterns beyond diffraction limits.

Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods

Scanning acoustic microscopy (SAM) provides high-resolution images of biological tissues. Since higher transducer frequencies limit penetration depth, image resolution enhancement techniques could help in maintaining sufficient lateral resolution without sacrificing penetration depth. Compared with existing SAM research, this work introduces two novelties. First, deep learning (DL) is used to improve lateral resolution of 180-MHz SAM images, comparing it with two deconvolution-based approaches. Second, 316-MHz images are used as ground truth in order to quantitatively evaluate image resolution enhancement. The samples used were mouse and rat brain sections. The results demonstrate that DL can closely approximate ground truth (NRMSE = 0.056 and PSNR = 28.4 dB) even with a relatively limited training set (four images, each smaller than 1 mm ×1 mm). This study suggests the high potential of using DL as a single image superresolution method in SAM.

Deconvolution in Intravascular Ultrasound to Improve Lateral Resolution

Intravascular ultrasound (IVUS) is an important diagnostic method for coronary disease. The lateral and axial resolutions of IVUS systems under study are typically ~120 and ~30 µm, respectively. The lateral resolution has a lower quality than the axial one and is restricted by the aperture size of transducers. In addition, this resolution is difficult to further improve physically. However, IVUS is inherently suitable for lateral deconvolution because of its circular imaging area. In this paper, magnitude-based deconvolution was demonstrated to be feasible in IVUS imaging to improve the lateral resolution. The deconvolution process was first simulated to determine the highest feasible resolution. Next, the method was applied to a real system to validate the feasibility. The lateral resolution was improved significantly, that is, 2°-separated targets could be discerned using a transducer with 4.2° -6 dB lateral resolution.

Acoustical structured illumination for super-resolution ultrasound imaging

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges.

Enhanced axial and lateral resolution using stabilized pulses

Ultrasound B-scan imaging systems operate under some well-known resolution limits. To improve resolution, the concept of stable pulses, having bounded inverse filters, was previously utilized for the lateral deconvolution. This framework has been extended to the axial direction, enabling a two-dimensional deconvolution. The modeling of the two-way response in the axial direction is discussed, and the deconvolution is performed in the in-phase quadrature data domain. Stable inverse filters are generated and applied for the deconvolution of the image data from Field II simulation, a tissue-mimicking phantom, and in vivo imaging of a carotid artery, where resolution enhancement is observed. Specifically, in simulation results, the resolution is enhanced by as many as 8.75 times laterally and 20.5 times axially considering the [Formula: see text] width of the autocorrelation of the envelope images.

Enhanced axial and lateral resolution using stabilized pulses

Ultrasound B-scan imaging systems operate under some well-known resolution limits. To improve resolution, the concept of stable pulses, having bounded inverse filters, was previously utilized for the lateral deconvolution. This framework has been extended to the axial direction, enabling a two-dimensional deconvolution. The modeling of the two-way response in the axial direction is discussed, and the deconvolution is performed in the in-phase quadrature data domain. Stable inverse filters are generated and applied for the deconvolution of the image data from Field II simulation, a tissue-mimicking phantom, and in vivo imaging of a carotid artery, where resolution enhancement is observed. Specifically, in simulation results, the resolution is enhanced by as many as 8.75 times laterally and 20.5 times axially considering the [Formula: see text] width of the autocorrelation of the envelope images.

A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct highquality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality.

Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Enhanced resolution pulse-echo imaging with stabilized pulses

Many pulse-echo imaging systems use focused beams to improve lateral resolution. The beam width is determined by the choice of source and apodization function, the frequency, and the physics of focusing. Postprocessing strategies to improve lateral resolution can be limited by the need for conditioning the mathematics of inverse filtering, due to instabilities. We present an analysis that defines key constraints on sampled versions of lateral beampatterns. Within these constraints are useful symmetric beampatterns, which, when properly sampled, can have a stable inverse filter. A framework for analysis and processing is described and applied to phantoms and tissues to demonstrate the improvements that can be realized.

Compressive Deconvolution in Medical Ultrasound Imaging

The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data.

Blind deconvolution for ultrasound sequences using a noninverse greedy algorithm

The blind deconvolution of ultrasound sequences in medical ultrasound technique is still a major problem despite the efforts made. This paper presents a blind noninverse deconvolution algorithm to eliminate the blurring effect, using the envelope of the acquired radio-frequency sequences and a priori Laplacian distribution for deconvolved signal. The algorithm is executed in two steps. Firstly, the point spread function is automatically estimated from the measured data. Secondly, the data are reconstructed in a nonblind way using proposed algorithm. The algorithm is a nonlinear blind deconvolution which works as a greedy algorithm. The results on simulated signals and real images are compared with different state of the art methods deconvolution. Our method shows good results for scatters detection, speckle noise suppression, and execution time.

Broadband sparse-array blind deconvolution using frequency-difference beamforming

Synthetic time reversal (STR) is a technique for blind deconvolution of receiving-array recordings of sound from an unknown source in an unknown multipath environment. It relies on generic features of multipath sound propagation. In prior studies, the pivotal ingredient for STR, an estimate of the source-signal's phase (as a function of frequency ω), was generated from conventional beamforming of the received-signal Fourier transforms, P(j)(ω), 1 ≤ j ≤ N, where N is the number of array elements. This paper describes how STR is implemented even when the receiving-array elements are many wavelengths apart and conventional beamforming is inadequate. Here, the source-signal's phase is estimated by beamforming P(j)(*)(ω(1))P(j)(ω(2)) at the difference frequency ω(2) - ω(1). This extension of STR is tested with broadband signal pulses (11-19 kHz) and a vertical 16-element receiving array having a 3.75-m-spacing between elements using simple propagation simulations and measured results from the FAF06 experiment involving 2.2 km of down slope propagation from 46 to 92 m water depth. The cross-correlation coefficient between the source-broadcast and STR-reconstructed-signal waveforms for the simulations and experiments are 98% and 91%-92%, respectively. In addition, frequency-difference beamforming can be used to determine signal-path-arrival angles that conventional beamforming cannot.

A blind deconvolution approach to ultrasound imaging

In this paper, a single-input multiple-output (SIMO) channel model is introduced for the deconvolution process of ultrasound imaging; the ultrasound pulse is the single system input and tissue reflectivity functions are the channel impulse responses. A sparse regularized blind deconvolution model is developed by projecting the tissue reflectivity functions onto the null space of a cross-relation matrix and projecting the ultrasound pulse onto a low-resolution space. In this way, the computational load is greatly reduced and the estimation accuracy can be improved because the proposed deconvolution model contains fewer variables. Subsequently, an alternating direction method of multipliers (ADMM) algorithm is introduced to efficiently solve the proposed blind deconvolution problem. Finally, the performance of the proposed blind deconvolution method is examined using both computer-simulated data and practical in vitro and in vivo data. The results show a great improvement in the quality of ultrasound images in terms of signal-to-noise ratio and spatial resolution gain.

A restoration framework for ultrasonic tissue characterization

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of l(2)-norm or (1)-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and l(1)-norm deconvolution techniques attests to the superiority of the proposed algorithm.

Efficient implementation of spatially-varying 3-D ultrasound deconvolution

There are sometimes occasions when ultrasound beamforming is performed with only a subset of the total data that will eventually be available. The most obvious example is a mechanically-swept (wobbler) probe in which the three-dimensional data block is formed from a set of individual B-scans. In these circumstances, non-blind deconvolution can be used to improve the resolution of the data. Unfortunately, most of these situations involve large blocks of three-dimensional data. Furthermore, the ultrasound blur function varies spatially with distance from the transducer. These two facts make the deconvolution process time-consuming to implement. This paper is about ways to address this problem and produce spatially-varying deconvolution of large blocks of three-dimensional data in a matter of seconds. We present two approaches, one based on hardware and the other based on software. We compare the time they each take to achieve similar results and discuss the computational resources and form of blur model that each requires.

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.

Robust finite impulse response beamforming applied to medical ultrasound

We previously described a beamformer architecture that replaces the single apodization weights on each receive channel with channel-unique finite impulse response (FIR) filters. The filter weights are designed to optimize the contrast resolution performance of the imaging system. Although the FIR beamformer offers significant gains in contrast resolution, the beamformer suffers from low sensitivity, and its performance rapidly degrades in the presence of noise. In this paper, a new method is presented to improve the robustness of the FIR beamformer to electronic noise as well as variation or uncertainty in the array response. A method is also described that controls the sidelobe levels of the FIR beamformer's spatial response by applying an arbitrary weighting function in the filter design algorithm. The robust FIR beamformer is analyzed using a generalized cystic resolution metric that quantifies a beamformer's clinical imaging performance as a function of cyst size and channel input SNR. Fundamental performance limits are compared between 2 robust FIR beamformers - the dynamic focus FIR (DF-FIR) beamformer and the group focus FIR (GF-FIR) beamformer - the conventional delay-and-sum (DAS) beamformer, and the spatial-matched filter (SMF) beamformer. Results from this study show that the new DF- and GF-FIR beamformers are more robust to electronic noise compared with the optimal contrast resolution FIR beamformer. Furthermore, the added robustness comes with only a slight loss in cystic resolution. Results from the generalized cystic resolution metric show that a 9-tap robust FIR beamformer outperforms the SMF and DAS beamformer until receive channel input SNR drops below -5 dB, whereas the 9-tap optimal contrast resolution beamformer's performance deteriorates around 50 dB SNR. The effects of moderate phase aberrations, characterized by an a priori root-mean-square strength of 28 ns and an a priori full-width at half-maximum correlation length of 3.6 mm, are investigate- d on the robust FIR beamformers. Full sets of robust FIR beamformer filter weights are constructed using an in silico model scanner and the L14-5/38 mm probe. Using the derived weights, a series of simulated point target and anechoic cyst B-mode images are generated to investigate further the potential increases in contrast resolution when using the robust FIR beamformers. Under the investigated conditions, the 7-tap optimal contrast resolution beamformer and the 7-tap robust beamformer with added SNR constraint increase lesion detectability by 247 and 137% compared with the conventional DAS beamformer, respectively. Finally, experimental phantom and in vivo images are produced using this novel receive architecture. The simulated and experimental images clearly show a reduction in clutter and an increase in contrast resolution compared with the conventionally beamformed images. This novel receive beamformer can be applied to any conventional ultrasound system where the system response is reasonably well characterized.

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.

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.

The modified convolution models of ultrasound echo signal

The ultrasound convolution model is addressed in this paper. Image formation process in the traditional model is expressed as a spatial-temporal convolution between the tissue signal and the ultrasonic system response. However, with the understanding of the existing widely-applied convolution model, we present modification since it omits the acoustical interactions inside the tissue. Consequently, under further analysis on the interactions and the reasonable assumptions of ultrasound propagations, two modified models are proposed, in which one takes the incident interaction into account and the other focuses on the backscattering interaction.

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.

Boundary Enhancement and Speckle Reduction for Ultrasound Images via Salient Structure Extraction

In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.

Modeling ultrasound imaging as a linear, shift-variant system

We solve the equation that governs acoustic wave propagation in an inhomogeneous medium to show that the radio-frequency (RF) ultrasound signal can be expressed as the result of filtering the scatterer field with a point-spread function. We extend the analysis to make the link between the RF ultrasound signal and the representation of ultrasound scatterers as vectors with small magnitude and random phase in the complex plane. Others have previously performed parts of this analysis. The contribution of the present paper is to provide a single, coherent treatment emphasizing the assumptions that have to be made and the physical consequences of the models derived. This leads to insights into the interaction of monopole and dipole scattering, useful techniques for simulating and analyzing speckle statistics in the complex plane and a new expression for the normalized covariance of the analytic RF ultrasound signal in terms of the complex envelope of the point-spread function.

Identification of ultrasound contrast agent dilution systems for ejection fraction measurements

Left ventricular ejection fraction is an important cardiac-efficiency measure. Standard estimations are based on geometric analysis and modeling; they require time and experienced cardiologists. Alternative methods make use of indicator dilutions, but they are invasive due to the need for catheterization. This study presents a new minimally invasive indicator dilution technique for ejection fraction quantification. It is based on a peripheral injection of an ultrasound contrast agent bolus. Left atrium and left ventricle acoustic intensities are recorded versus time by transthoracic echocardiography. The measured curves are corrected for attenuation distortion and processed by an adaptive Wiener deconvolution algorithm for the estimation of the left ventricle impulse response, which is interpolated by a monocompartment exponential model for the ejection fraction assessment. This technique measures forward ejection fraction, which excludes regurgitant volumes. The feasibility of the method was tested on a group of 20 patients with left ventricular ejection fractions going from 10% to 70%. The results are promising and show a 0.93 correlation coefficient with echographic bi-plane ejection fraction measurements. A more extensive validation as well as an investigation on the method applicability for valve insufficiency and right ventricular ejection fraction quantification will be an object of future study.