Deconvolution Of Ultrasound Images Research Articles

Semi-Blind Ultrasound Image Deconvolution from Compressed Measurements

The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data.

Ultrasound Image Deconvolution Based on Energy Functional

Ultrasound Image Deconvolution Based on Energy Functional

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on Generalized Gaussian Priors.

This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.

A simulation study on the choice of regularization parameter in ℓ2-norm ultrasound image restoration.

Ultrasound image deconvolution has been widely investigated in the literature. Among the existing approaches, the most common are based on ℓ2-norm regularization (or Tikhonov optimization) or the well-known Wiener filtering. However, the success of the Wiener filter in practical situations largely depends on the choice of the regularization hyperparameter. An appropriate choice is necessary to guarantee the balance between data fidelity and smoothness of the deconvolution result. In this paper, we revisit different approaches for automatically choosing this regularization parameter and compare them in the context of ultrasound image deconvolution via Wiener filtering. Two synthetic ultrasound images are used in order to compare the performances of the addressed methods.

Generalized TV and Sparse Decomposition of the Ultrasound Image Deconvolution Model Based on Fusion Technology

Ultrasound image deconvolution involves noise reduction and image feature enhancement, denoising need equivalent the low-pass filtering, image feature enhancement is to strengthen the high-frequency parts, these two requirements are often combined together. It is a contradictory requirement that we must be reasonable balance between these two basic requirements. Image deconvolution method of partial differential equation model is the method based on diffusion theory, and sparse decomposition deconvolution is image representation-based method. The mechanisms of these two methods are not the same, effect of these two methods own characteristics. In contourlet transform domain, we combine the strengths of the two deconvolution method together by image fusion, and introduce the entropy of local orientation energy ratio into fusion decision-making, make a different treatment according to the actual situation on the low-frequency part of the coefficients and the high-frequency part of the coefficient. As deconvolution process is inevitably blurred image edge information, we fusion the edge gray-scale image information to the deconvolution results in order to compensate the missing edge information. Experiments show that our method is better than the effect separate of using deconvolution method, and restore part of the image edge information.

Estimation of speed of sound in dual-layered media using medical ultrasound image deconvolution

The speed of sound in soft tissues is usually assumed to be 1540 m/s in medical pulse-echo ultrasound imaging systems. When the true speed is different, the mismatch can lead to distortions in the acquired images, and so reduce their clinical value. Previously we reported a new method of sound-speed estimation in the context of image deconvolution. Unlike most other sound-speed estimation methods, this enables the use of unmodified ultrasound machines and a normal scanning pattern. Our approach was validated for largely homogeneous media with single sound speeds. In this article, we demonstrate that sound speeds of dual-layered media can also be estimated through image deconvolution. An ultrasound simulator has been developed for layered media assuming that, for moderate speed differences, the reflection at the interface may be neglected. We have applied our dual-layer algorithm to simulations and in vitro phantoms. The speed of the top layer is estimated by our aforesaid method for homogeneous media. Then, when the layer boundary position is known, a series of deconvolutions are carried out with dual-layered point-spread functions having different lower-layer speeds. The best restoration is selected using a correlation metric. The error level (e.g., a mean error of −9 m/s with a standard deviation of 16 m/s) for in vitro phantoms is found to be not as good as that of our single-speed algorithm, but is comparable to other local speed estimation methods where the data acquisition may not be as simple as in our proposed method.

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.

Three-dimensional blind deconvolution of ultrasound images.

Three-dimensional ultrasound images are blurred by the ultrasound pulse through the convolution between the 3-D tissue signal and the 3-D pulse. The blurring reduces the spatial resolution of the 3-D ultrasound images and, consequently, their diagnostic value. This paper presents a method for 3-D blind homomorphic deconvolution of medical 3-D ultrasound images to improve their spatial resolution. The blind estimate of the 3-D pulse is necessary because the pulse changes in spatial extent and frequency composition as it passes through the tissues and because the pulse is not separable in its spatial dimensions. The method was tested on a 3-D image of a phantom with anechoic spheres of known size in a uniform diffuse scattering matrix. The spheres were clearly better defined and had volumes much closer to the true volume in the deconvolved image than in the original image.

Erratum - Correction to: "Comparison of Cepstrum-Based Methods for Radial Blind Deconvolution of Ultrasound Images", Vol. 44, No. 3, p. 666.

Erratum - Correction to: "Comparison of Cepstrum-Based Methods for Radial Blind Deconvolution of Ultrasound Images", Vol. 44, No. 3, p. 666.

Higher order versus second order statistics in ultrasound image deconvolution

We recently proposed a method for the estimation of imaging distortions associated with ultrasound images, based on the higher-order statistics (HOS) of radio frequency data. In this correspondence, we utilize the HOS-based estimated distortions to deconvolve ultrasound images of the breast. We also estimate imaging distortions based on the second-order statistics (SOS) of radio frequency ultrasound data and subsequently utilize them to deconvolve the same breast images. Both subjective and objective measures suggest that deconvolution with HOS-based distortion estimates led to significantly higher resolution gains as compared to the gains achieved when SOS-based distortion estimates were used.

Comparison of cepstrum-based methods for radial blind deconvolution of ultrasound images

This paper compares the performance of seven different cepstrum-based methods for radial blind deconvolution of medical ultrasound images. The first is the generalized cepstrum method. The second is the spectral root cepstrum method. These methods have received little attention so far. The last five methods are all based on the complex cepstrum, but different computational techniques in the spatial and frequency domain are employed. Using in vivo radio frequency data from a clinical scanner, the generalized cepstrum method gave the best images closely followed by the complex cepstrum using phase unwrapping or polynomial rooting. The complex cepstrum method using higher-order statistics was ranked as low as number five. These results are an important guideline for selecting a specific cepstrum-based radial deconvolution method for implementation in ultrasound scanners.

Higher order spectra based deconvolution of ultrasound images

We address the problem of improving the spatial resolution of ulrasound images through blind deconvolution. The ultrasound image formation process in the RF domain can be expressed as a spatio-temporal convolution between the tissue response and the ultrasonic system response, plus additive noise. Convolutional components of the dispersive attenuation and aberrations introduced by propagating through the object being imaged are also incorporated in the ultrasonic system response. Our goal is to identify and remove the convolutional distortion in order to reconstruct the tissue response, thus enhancing the diagnostic quality of the ultrasonic image. Under the assumption of an independent, identically distributed, zero-mean, non-Gaussian tissue response, we were able to estimate distortion kernels using bicepstrum operations on RF data. Separate 1D distortion kernels were estimated corresponding to axial and lateral image lines and used in the deconvolution process. The estimated axial kernels showed similarities to the experimentally measured pulse-echo wavelet of the imaging system. Deconvolution results from B-scan images obtained with clinical imaging equipment showed a 2.5-5.2 times gain in lateral resolution, where the definition of the resolution has been based on the width of the autocovariance function of the image. The gain in axial resolution was found to be between 1.5 and 1.9.