Ultrasound image reconstruction by two-dimensional blind total variation deconvolution

The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a "hybridization" of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the "hybrid" approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolutioh algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.

There are a number of technical situations where inversion of measured data involves blind deconvolution (i.e. the point spread function is unknown a priori) in three (or more) dimensions. We show that a unique solution exists for three-dimensional (3D) blind deconvolution for a particular sampling of the Fourier transform of a blurred image whose average density is less than the Nyquist density. This suggests that 3D blind deconvolution problems may be easier to solve than 2D problems. Experiments using an iterative blind deconvolution algorithm indicate that convergence is faster for the 3D case than for the 2D case, and that reducing the sampling density has a rather small effect on the quality of reconstructions. >

We propose a new method of blind deconvolution (BD) suitable for the recovery of truncated blurred images. In practical cases we often encounter truncated images, but they are difficult to treat properly with conventional BD algorithms, since nontruncated blurred images are assumed. The proposed method is based on simulated annealing (SA), to which we introduce an adaptive masking process. After a truncated segment is cut out from the blurred image, the sharp edges surrounding the segment are removed by multiplying the initial window, whose shape is determined with the initial estimate of the point spread function (PSF). A more appropriate estimate for the PSF is obtained from the masked segment based on the SA algorithm, and according to the new estimate of the PSF, the shape of the window is varied. These procedures are iterated until we finally obtain the best possible estimate of the PSF. In the last step, the entire blurred image is deconvolved with the final estimate of the PSF to obtain the recovered image. The effectiveness of the proposed method is confirmed by computer simulation.

An iterative blind deconvolution algorithm for degrade image is presented in this paper. The algorithm including two step, namely, the estimation of the point spread function of degrade image and the restoration using estimated point spread function. Two different Hopfield neural networks are built for realize the two steps. An iterative procedure is used to control the restoration process. The simulation results indicate that the method is effective for blind deconvolution with high convergence speed.

A weighted total variation algorithm was proposed for solving some blind deconvolution problems,and then we got the existence of energy functional minimum and educed corresponding thermal flow.In computer simulations,the algorithm performs well even when we have no more accurate prior knowledge about point spread function and the original true image,besides some other constrains.Experiments show that the proposed algorithm is more effective and performs better than the total variation blind deconvolution method proposed.

We propose to use the blind deconvolution and its modification to extract the point spread function (PSF) of layered metamaterials from a SNOM measurement. The measurement results are processed using the blind deconvolution algorithm to reconstruct the real-valued non-coherent PSF, or using the modified blind deconvolution introduced in this paper to reconstruct the complex-valued coherent PSF. The two algorithms are tested in simulations with a layered metamaterial deposited on a thin metallic mask with test apertures. We show that the modified algorithm is capable of recovering the approximate shape of complex PSF with a sub-wavelength full width at half maximum from a measurement in which the apertures are larger than the wavelength.

The visual quality of ultrasound (US) images is crucial for clinical diagnosis and treatment. The main source of image quality degradation is the inherent speckle noise generated during US image acquisition. Current deep learning-based methods cannot preserve the maximum boundary contrast when removing noise and speckle. In this paper, we address the issue by proposing a novel wavelet-based generative adversarial network (GAN) for real-time high-quality US image reconstruction, viz. WGAN-DUS. First, we propose a batch normalization module (BNM) to balance the importance of each sub-band image and fuse sub-band features simultaneously. Then, a wavelet reconstruction module (WRM) integrated with a cascade of wavelet residual channel attention block (WRCAB) is proposed to extract distinctive sub-band features used to reconstruct denoised images. A gradual tuning strategy is proposed to fine-tune our generator for better despeckling performance. We further propose a wavelet-based discriminator and a comprehensive loss function to effectively suppress speckle noise and preserve the image features. Besides, we have designed an algorithm to estimate the noise levels during despeckling of real US images. The performance of our network was then evaluated on natural, synthetic, simulated and clinical US images and compared against various despeckling methods. To verify the feasibility of WGAN-DUS, we further extend our work to uterine fibroid segmentation with the denoised US image of the proposed approach. Experimental result demonstrates that our proposed method is feasible and can be generalized to clinical applications for despeckling of US images in real-time without losing its fine details.

Generally, blind super-resolution with unknown blurs is treated as an optimization problem. This involves a cost function, composed of terms accounting for changes in image and point spread function (PSF), which usually undergoes regularization due to the ill-posedness of the problem. In this paper, we introduce a novel regularization term for the PSF such that the spectral change in the image caused by degradation is also included. This is based on the fact that the presence of PSF in images affects the frequency component concentration. This cost function is optimized with respect to the image and the PSF in an alternating manner. Experiment results show that the proposed method is effective based on an objective evaluation method and that its PSF estimation accuracy is competitive in comparison with the recently proposed parametric method.

A new blind deconvolution method with additional phase spectral constraints for the blurred image is discussed in this paper. The blurred image is a convolution of an original image and a point-spread function (PSF). The proposed method is based on the following iterative procedures, i.e., a projection onto a frequency space satisfying phase spectrum constraints, a minimization of a cost function and a projection onto an image space satisfying the support and nonnegativity constraints. This method restores the original image and the PSF stably with high accuracy. The effectiveness of the proposed method is verified by applying it to some blind deconvolution problems of images. The experimental results show that the performance of the proposed method is drastically improved in comparison with the conventional blind deconvolution method.

This study addresses the blind image deconvolution which uses only blurred image and less point spread function (PSF) information to restore the original image. To identify the blind image it is a very important step for restoring the image. Therefore, the first step is to look for PSF model. In this paper, particle swarm optimization (PSO) is utilized to seek the unknown PSF. The objective function is based on the wavelet transform. It can identify the parameters of PSF exactly. Finally, the feasibility and validity of proposed algorithm are demonstrated by several simulations.

In this paper we propose a blind deconvolusion method to enhance the resolution of images obtained by near-field microwave nondestructive techniques using an open ended rectangular waveguide probe. In fact, we model such imags to be the result of a convolution of the real input images with a point spread function (PSF). This PSF depends mainly on the dimensions of the waveguide, the operating frequency, the nature of the object under test and standoff distance between the waveguide and the object. Unfortunately, it is very difficult to model this PSF from the physical data. For this reason, we consider the problem as a blind deconvolution. The proposed method is based on regularization, and the solution is obtained iteratively, by successive estimation of the input and the PSF. The algorithm is initialized with a PSF obtained from a very simplified physical model. The performance of the proposed method is evaluated on some real data. Several examples of real image enhancement will be presented.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Blind deconvolution subject to sparse representation for fluorescence microscopy

Image deconvolution is a standard numerical procedure used in medical ultrasound imaging for improving the resolution and contrast of diagnostic sonograms. However, due to the intrinsic bandlimitedness of ultrasound scanners and the adverse effect of measurement noises, image deconvolution is known to be exceedingly sensitive to the errors incurred during inference of the point spread function (PSF) that characterizes the imaging system in use. In this case, even the slightest errors in specification of the PSF are likely to result in significant artifacts, rendering the reconstructed images worthless. To address the aforementioned problem, this paper describes a new method for blind deconvolution of ultrasound images, in which the errors due to inaccuracies in specification of the PSF are eliminated concurrently with estimation of tissue reflectivity directly from its associated radio-frequency data. A principal derivation and justification of the proposed method are supported by experimental results which demonstrate the effectiveness and viability of the new technique.

Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system Point Spread Function (PSF). In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reconstruction is introduced. More specifically, the proposed formulation is a regularized inverse problem including two linear models for beamforming and deconvolution plus additional sparsity constraint. We take advantage of the alternating direction method of multipliers algorithm to find the solution of the joint optimization problem. The performance evaluation is presented on a set of publicly available simulations, real phantoms, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data. As compared to Delay-And-Sum (DAS) beamforming, simulation results indicate improvements of 45% and 44% in terms of axial and lateral resolution, respectively. Moreover, the proposed method improves the contrast of simulation data by 6.7% in comparison to DAS. The superiority of the proposed approach in comparison with the sequential approach as well as the state-of-the-art beamforming and deconvolution approaches is also shown.

Over the past decade, scanning fluorescence microscopy based on two-photon excitation has become an important branch of microscopic bio-imaging. Compared to traditional scanning techniques, two-photon microscopy offers a number of distinct advantages. First, scanning of the point-like excitation spot used for imaging results in images with excellent axial depth discrimination. In addition, the limited extent of the excitation volume also limits specimen photo-damage to the focal volume. Finally, the long, near-infixed wavelengths used for sample excitation allow in-depth, non-invasive imaging of optically turbid biological samples. For in-depth imaging, the microscopic objective and often optically heterogeneous biological specimen forms a complex system. To optimize imaging quality in two-photon microscopy, an understanding of the point-spread-function (PSF) is essential. In this work, we attempted to characterize the two-photon PSF by two methods: direct imaging of 0.1 ym fluorescent microspheres and multicolor imaging of 2 ym green fluorescent microspheres in a uniform blue fluorescent background. In both measurements, the turbidity of the surrounding medium was varied by changing the concentration of Liposyn III, a scattering component in the specimen. We found that at discrete Liposyn III concentrations between 0 and 2%, the PSF widths were not affected by the amount of scatterers present. However, the imaged contrast continued to degrade as a function of the amount of scatter. This suggests that the broadening of the tail region of the PSF can be the cause of image contrast loss. We will also discuss the possibility of using blind- deconvolution as a method to obtain PSF information in complex biological specimen.