Tissue Reflectivity Function Research Articles

Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model

In the quest for higher acquisition rates of ultrasound images, the simultaneous emission of encoded waves has the potential to overcome the trade-off between acquisition time and image quality. However, the lack of fully orthogonal codes has led to the use of forward models and inverse problem approaches to estimate the imaged medium. Nonetheless, due to some simplifying assumptions on which these models rely, the previously stated trade-off still appears in these acquisition/reconstruction schemes. In this paper, a forward model for ultrasound wave propagation inside a scattering medium is developed for the simultaneous coded emission of plane waves. The tissue reflectivity function of the imaged medium is estimated by solving an ℓ1-regularized version of the corresponding inverse problem. The proposed method is evaluated in silico and in vitro. We demonstrate that this method outperforms the conventional technique that consists of successive emissions of plane waves, reconstruction using delay and sum (DAS), and coherent compounding. In silico, the ability to separate close scatterers is improved by a factor of four in the axial direction and by a factor of 2.5 in the lateral direction. In vitro, the spatial resolution at −6 dB is decreased by a factor of seven. These results suggest that the proposed method could be a valuable tool, particularly for ultrasound imaging of sparse mediums such as in ultrasound localization microscopy.

Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

This paper presents a novel beamforming approach based on deep learning to get closer to the ideal Point Spread Function (PSF) in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct a high-quality version of Tissue Reflectivity Function (TRF) from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, a model for the TRF is introduced by setting the imaging PSF as an isotropic (i.e., circularly symmetric) 2D Gaussian kernel convolved with a cosine function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed output is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired output. We exploit step by step training from coarse (mean square error) to fine (ℓ0.2) loss functions. The proposed method is trained on 1174 simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and in vivo test data without any further fine-tuning. Simulation test results show an improvement of 37.5% and 65.8% in terms of axial and lateral resolution as compared to Delay-And-Sum (DAS) results, respectively. The contrast is also improved by 33.7% in comparison to DAS. Furthermore, the reconstructed in vivo images confirm that the trained mapping function does not need any fine-tuning in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.

Ultrasound Image Deconvolution Using Fundamental and Harmonic Images

Ultrasound (US) image restoration from radio frequency (RF) signals is generally addressed by deconvolution techniques mitigating the effect of the system point spread function (PSF). Most of the existing methods estimate the tissue reflectivity function (TRF) from the so-called fundamental US images, based on an image model assuming the linear US wave propagation. However, several human tissues or tissues with contrast agents have a nonlinear behavior when interacting with US waves leading to harmonic images. This work takes this nonlinearity into account in the context of TRF restoration, by considering both fundamental and harmonic RF signals. Starting from two observation models (for the fundamental and harmonic images), TRF estimation is expressed as the minimization of a cost function defined as the sum of two data fidelity terms and one sparsity-based regularization stabilizing the solution. The high attenuation with a depth of harmonic echoes is integrated into the direct model that relates the observed harmonic image to the TRF. The interest of the proposed method is shown through synthetic and in vivo results and compared with other restoration methods.

Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images

Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm. This new approach is combined with a forward-backward step and a preconditioning strategy to accelerate the convergence, and with a method based on the majorization-minimization principle to solve the inner nonconvex minimization problems. As demonstrated in numerical experiments conducted on both simulated and in vivo ultrasound images, the proposed method provides high-quality restoration and segmentation results and is up to six times faster than an existing Hamiltonian Monte Carlo method.

Attenuation estimation of soft tissue with reference-free minimization of system effects

Estimation of ultrasonic attenuation coefficient (AC) is essential for quantifying and characterizing the features of tissue microstructure. In the conventional AC estimation methods, a well-specified reference phantom is commonly used for minimizing the diffraction and transmit pulse related effects on the ultrasound radio-frequency (RF) signal. In this paper, a novel AC estimation technique is proposed avoiding the need of using any reference data, where the undesired system effects on the RF data are minimized through point spread function (PSF) separation and band-pass filtering of the envelope signal of the tissue reflectivity function (TRF). An improved and computationally efficient non-parametric cepstrum-based technique is used for separating the TRF and PSF from the measured RF signal. The Hilbert transform based temporal envelope is introduced to smooth out the unwanted effects of discontinuity and noise in the TRF and PSF signals. Finally, a band-pass filter based log power approximation of the TRF-envelope is applied to estimate the center frequency component of the attenuating power spectra with reduced diffraction effect. Assuming continuity of AC within a small uniform region, an exponentially weighted-average of logarithmic signal power of the neighboring blocks at the center frequency is measured for different depths, with a view to fit a regression line for obtaining an average AC value from its slope. Comparative results of the proposed reference-free AC estimation method with other conventional reference-based methods are presented for tissue-mimicking (TM) phantoms, in vivo breast and liver data. For the TM phantoms, the AC estimates using the new algorithm are within 10% deviation of the actual values. The obtained results for the breasts, normal livers, and fatty livers are 0.44 ± 0.23 dB/cm-MHz, 0.55 ± 0.21 dB/cm-MHz, and 0.61 ± 0.20 dB/cm-MHz, respectively, which are consistent with the literature-reported AC values. Different from reference-based methods, the novel technique is free from the bias that may result from the dissimilarity between acoustic property of the reference and sample.

Ultrasonic tissue reflectivity function estimation using correlation constrained multichannel flms algorithm with missing rf data

Poor resolution of ultrasound images due to convolution of the tissue reflectivity function (TRF) with the system point spread function (PSF) is a major issue in medical ultrasound imaging. In this paper, we propose a correlation constrained missing-data estimation based blind multichannel frequency-domain least-mean-squares (md-bMCFLMS) algorithm to undo the effect of PSF on the ultrasound radio-frequency (RF) data. In the first step, a block-based MCFLMS (bMCFLMS) algorithm is proposed to estimate the TRFs and the PSF, which are used in the second step to estimate the missing data. This missing data is used in the md-bMCFLMS algorithm to construct a modified cost function for further improvement of the image resolution. To account for the nonstationarity of the PSF, unlike the blocking approach described in the literature, we introduce a time-efficient blocking method in this paper. The blocking approach described here uses a block position independent fixed size matrix and can be implemented parallely. The bMCFLMS algorithm, however, shows misconvergence due to both channel noise and propagation of TRF estimation error from the previous blocks. This phenomenon is more intense in the case of md-bMCFLMS algorithm because of increased estimation error. To address this problem, a novel constraint based on the correlation between the measured RF data and estimated TRF is proposed in this paper. The efficacy of our proposed blind deconvolution algorithm is measured using simulation phantom and in-vivo data. The proposed md-bMCFLMS algorithm shows normalised projection misalignment (NPM) improvement of about dB and resolution gain (RG) improvement of 1.14 ∼ 6.4 dB compared to other techniques in the literature. Moreover, because of the frequency-domain implementation it is computationally more efficient, fast converging and robust than its time-domain counterpart l1-bMCLMS algorithm reported in the literature.

An envelope signal based deconvolution algorithm for ultrasound imaging

To improve the quality of medical ultrasound images, a number of restoration methods based on demodulated signals have been proposed in the literature. However, due to the shift of center frequency of transmitted ultrasound pulses at different penetration depth in a lossy medium, it is hard to determine the exact center frequency at a specified position so to achieve satisfactory demodulation. In this paper, this problem is dealt with by a novel restoration method based on envelope models of the radio frequency (RF) and the point spread function (PSF) signals. To cope with the ill inverse problem caused by the narrow band PSF, an envelop signal based sparse regularized deconvolution model is derived under a sparsity assumption of the tissue reflectivity function (TRF). Furthermore, a two-step iterative shrinkage/thresholding (TwIST) method based alternating minimization approach is applied to compute the optimal solution of the proposed deconvolution problem. Finally, the robustness and the practicability of the proposed method are demonstrated by a series of experiments on both numerical simulation and in vivo data. The experimental results show that the proposed method can achieve significant improvement of the ultrasound images in terms of the resolution gain and signal-to-noise ratio (SNR).

A novel approach to the 2-D blind deconvolution problem in medical ultrasound

The finite frequency bandwidth of ultrasound transducers and the nonnegligible width of transmitted acoustic beams are the most significant factors that limit the resolution of medical ultrasound imaging. Consequently, in order to recover diagnostically important image details, obscured due to the resolution limitations, an image restoration procedure should be applied. The present study addresses the problem of ultrasound image restoration by means of the blind-deconvolution techniques. Given an acquired ultrasound image, algorithms of this kind perform either concurrent or successive estimation of the point-spread function (PSF) of the imaging system and the original image. In this paper, a blind-deconvolution algorithm is proposed, in which the PSF is recovered as a preliminary stage of the restoration problem. As the accuracy of this estimation affects all the following stages of the image restoration, it is considered as the most fundamental and important problem. The contribution of the present study is twofold. First, it introduces a novel approach to the problem of estimating the PSF, which is based on a generalization of several fundamental concepts of the homomorphic deconvolution. It is shown that a useful estimate of the spectrum of the PSF can be obtained by applying a proper smoothing operator to both log-magnitude and phase of the spectra of acquired radio-frequency (RF) images. It is demonstrated that the proposed approach performs considerably better than the existing homomorphic (cepstrum-based) deconvolution methods. Second, the study shows that given a reliable estimate of the PSF, it is possible to deconvolve it out of the RF-image and obtain an estimate of the true tissue reflectivity function, which is relatively independent of the properties of the imaging system. The deconvolution was performed using the maximum a-posteriori (MAP) estimation framework for a number of statistical priors assumed for the reflectivity function. It is shown in a series of in vivo experiments that reconstructions based on the priors, which tend to emphasize the "sparseness" of the tissue structure, result in solutions of higher resolution and contrast.

Robust estimation of ultrasound pulses using outlier-resistant de-noising

A different approach to the problem of estimation of the ultrasound pulse spectrum, which usually arises as a part of ultrasound image restoration algorithms, is presented. It is shown that this estimation problem can be reformulated in terms of a de-noising problem. In this formulation, the log-spectrum of a radio-frequency line (RF-line) is viewed as a noisy measurement of the signal that needs to be estimated, i.e., the ultrasound pulse log-spectrum. The log-spectrum of the tissue reflectivity function (i.e., tissue response) is considered as the noise to be rejected. The contribution of the paper is twofold. First, it provides statistical description of the reflectivity function log-spectrum for the case, when the samples of the reflectivity function are independent identically distributed (i.i.d.) Gaussian random variables. Moreover, it is shown that the problem of the pulse spectrum recovery is essentially a de-noising problem. Consequently, it is suggested to solve the problem within the framework of the de-noising by wavelet shrinkage. Second, a computationally efficient algorithm is proposed for the pulse-spectrum estimation, which can be viewed as a modified version of the classical Donoho's three-step de-noising procedure. This modification is necessary, because of specific properties of the noise to be rejected. It is shown, that whenever the samples of the reflectivity function can be assumed to be i.i.d. Gaussian random variables, the samples of its log-spectrum obey the Fisher-Tippet distribution. For this type of noise, straightforward implementation of the standard de-noising can cause serious estimation errors. In order to overcome this difficulty, an outlier-resistant de-noising is performed. The unique properties of this modified de-noising algorithm allow estimating the pulse spectrum adaptively to its properties, as they are continuously influenced by the frequency-dependent attenuation process. The performance of the proposed algorithm is examined in a series of computer-simulations. It is shown that this algorithm, developed on the assumption of the "Gaussian" reflectivity function, remains applicable for broader classes of distributions. The results obtained in a series of in vivo experiments reveal superior performance of the novel approach over some of alternative estimation techniques, e.g., cepstrum-based estimation.