Towards dynamic contrast specific ultrasound tomography

We report on the first study demonstrating the ability of a recently-developed, contrast-enhanced, ultrasound imaging method, referred to as cumulative phase delay imaging (CPDI), to image and quantify ultrasound contrast agent (UCA) kinetics. Unlike standard ultrasound tomography, which exploits changes in speed of sound and attenuation, CPDI is based on a marker specific to UCAs, thus enabling dynamic contrast-specific ultrasound tomography (DCS-UST). For breast imaging, DCS-UST will lead to a more practical, faster, and less operator-dependent imaging procedure compared to standard echo-contrast, while preserving accurate imaging of contrast kinetics. Moreover, a linear relation between CPD values and ultrasound second-harmonic intensity was measured (coefficient of determination = 0.87). DCS-UST can find clinical applications as a diagnostic method for breast cancer localization, adding important features to multi-parametric ultrasound tomography of the breast.</p

Ultrasonic array doppler sensing for human movement classification

Classification of human movements is an important problem in healthcare and well-being applications. An ultrasonic array Doppler sensing method is proposed for classifying movements from a given set. The proposed method uses velocity and angular information derived from Doppler frequencies and direction-of-arrival (DoA) by processing the signals at the receiver sensor array. Doppler frequency estimation is done by obtaining an initial estimate based on the Fourier transform in conjunction with a predictive tracker. A Root-MUSIC algorithm is used at the estimated Doppler frequencies to obtain DoA corresponding to the dominating moving object. Using speed, direction, and angle as features, a Bayesian classifier is employed to distinguish between a set of movements. The performance of the proposed method is evaluated using an analytical model of arm movements and also using experimental data sets. The proposed ultrasonic Doppler array sensor and processing methods provide a new, compact solution to human arm movement classification

Blind source separation for clutter and noise suppression in ultrasound imaging:review for different applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several 'source' signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection

Ultrasound coefficient of nonlinearity imaging

Imaging the acoustical coefficient of nonlinearity, ß, is of interest in several healthcare interventional applications. It is an important feature that can be used for discriminating tissues. In this paper, we propose a nonlinearity characterization method with the goal of locally estimating the coefficient of nonlinearity. The proposed method is based on a 1-D solution of the nonlinear lossy Westerfelt equation, thereby deriving a local relation between ß and the pressure wave field. Based on several assumptions, a ß imaging method is then presented that is based on the ratio between the harmonic and fundamental fields, thereby reducing the effect of spatial amplitude variations of the speckle pattern. By testing the method on simulated ultrasound pressure fields and an in vitro B-mode ultrasound acquisition, we show that the designed algorithm is able to estimate the coefficient of nonlinearity, and that the tissue types of interest are well discriminable. The proposed imaging method provides a new approach to ß estimation, not requiring a special measurement setup or transducer, that seems particularly promising for in vivo imaging

Ultrasound coefficient of nonlinearity imaging

Imaging the acoustical coefficient of nonlinearity, ß, is of interest in several healthcare interventional applications. It is an important feature that can be used for discriminating tissues. In this paper, we propose a nonlinearity characterization method with the goal of locally estimating the coefficient of nonlinearity. The proposed method is based on a 1-D solution of the nonlinear lossy Westerfelt equation, thereby deriving a local relation between ß and the pressure wave field. Based on several assumptions, a ß imaging method is then presented that is based on the ratio between the harmonic and fundamental fields, thereby reducing the effect of spatial amplitude variations of the speckle pattern. By testing the method on simulated ultrasound pressure fields and an in vitro B-mode ultrasound acquisition, we show that the designed algorithm is able to estimate the coefficient of nonlinearity, and that the tissue types of interest are well discriminable. The proposed imaging method provides a new approach to ß estimation, not requiring a special measurement setup or transducer, that seems particularly promising for in vivo imaging

Shear wave viscoelasticity imaging using local system identification

Tissue elasticity is an important parameter which relates to the pathological state of soft tissue. Fibrotic lesions or malignant tumors are known to be notoriously stiff compared to benign tissue. Shear wave elastography can provide a fully quantitative measure of lesion stiffness by estimating the speed at which acoustically induced shear waves propagate through the material. This speed is in turn related to the Young's modulus. In soft tissue, elasticity is generally accompanied by viscosity, leading to dispersion of the shear wave. For the detection and characterization of malignant lesions, viscosity has in fact diagnostic value. Here, we describe a new method that enables imaging not only elasticity but also viscosity from shear wave elastography by local model-based system identification. We show that the proposed method can be applied effectively to standard shear wave acquisitions, and is able to generate high-resolution parametric maps of the viscoelastic material properties in an in-vitro setting

Viscoelasticity mapping by identification of local shear wave dynamics

Estimation of soft tissue elasticity is of interest in several clinical applications. For instance, tumors and fibrotic lesions are notoriously stiff compared with benign tissue. A fully quantitative measure of lesion stiffness can be obtained by shear wave (SW) elastography. This method uses an acoustic radiation force to produce laterally propagating SWs that can be tracked to obtain the velocity, which in turn is related to Young's modulus. However, not only elasticity, but also viscosity plays an important role in the propagation process of SWs. In fact, viscosity itself is a parameter of diagnostic value for the detection and characterization of malignant lesions. In this paper, we describe a new method that enables imaging viscosity from SW elastography by local model-based system identification. By testing the method on simulated data sets and performing in vitro experiments, we show that the ability of the proposed technique to generate parametric maps of the viscoelastic material properties from SW measurements, opening up new possibilities for noninvasive tissue characterization

Compressed sensing for ultrasound computed tomography

Ultrasound computed tomography (UCT) allows the reconstruction of quantitative tissue characteristics, such as speed of sound, mass density, and attenuation. Lowering its acquisition time would be beneficial; however, this is fundamentally limited by the physical time of flight and the number of transmission events. In this letter, we propose a compressed sensing solution for UCT. The adopted measurement scheme is based on compressed acquisitions, with concurrent randomised transmissions in a circular array configuration. Reconstruction of the image is then obtained by combining the born iterative method and total variation minimization, thereby exploiting variation sparsity in the image domain. Evaluation using simulated UCT scattering measurements shows that the proposed transmission scheme performs better than uniform undersampling, and is able to reduce acquisition time by almost one order of magnitude, while maintaining high spatial resolution

Shear-wave imaging of viscoelasticity using local impulse response identification

Imaging technologies that allow assessment of the elastic properties of soft tissue provide clinicians with an important asset for several diagnostic applications. A quantitative measure of stiffness can be obtained by shear-wave (SW) elasticity imaging, a method that uses acoustic radiation force to produce laterally-propagating shear waves that can be tracked to obtain the velocity, which in turn is related to the shear modulus. If one considers the medium to be purely elastic, its local shear modulus can be estimated by determining the local SW velocity. However, this assumption does not hold for many tissue types, whenever the shear viscosity plays an important role. In fact, there is increasing evidence that viscosity itself could be an important marker for malignancy [1]. In this work, we therefore aim at providing a joint local estimate of tissue elasticity and viscosity based on SW elastography