Enhancement of two-dimensional acoustic source identification with Fourier-based deconvolution beamforming
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Abstract:
When utilizing conventional regular focus point distributions to define a relatively large source region, Fourier-based deconvolution beamforming, an attractive acoustic source identification technique, would suffer from some limitations: 1) significantly deteriorative location and quantification accuracy for sources away from the center of the focus region; 2) pronounced sidelobe contaminations. The arch-criminal is the assumption that the point spread function (PSF) is definitely shift-invariant over the entire focus region fails to be satisfied well. This paper focuses on remedying these limitations for two-dimensional (2D) acoustic source identification. First and foremost, a novel focus point generation approach is introduced, which can generate unconventional irregular 2D focus point distributions tending to make PSF more shift-invariant. Additionally, a sidelobe suppression approach is suggested. Effects of these approaches are examined both with computer simulations and experimentally. This study provides the feasibility of using Fourier-based deconvolution beamforming to accurately and efficiently identify acoustic sources in a relatively large region.Keywords:
Point source
Identification
Point spread function
The deconvolution for 3D image of wide-field microscope need to process very large data size,and it spend a lot of time.3D PSF(three dimensional point spread function),out-of-focus images,optical section images and restored images deconvolved by different 3D PSF were estimated and analyzed.It was proved further that the disturbance of out-of-focus light to in-focus images was from out-of-focus images of both sides.It was proposed that 3D microscopy deconvolution could get more perfect effect of image restoration and the runtime could be spend less through selecting appropriate size of 3D PSF.And a new standard for estimating the definition or fuzzy degree of image was proposed,and it was applied to estimation of out-of-focus images,optical section images and restored images.
Point spread function
Depth of field
Optical transfer function
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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 in vivo 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.
Point spread function
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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. 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 in vivo data. Furthermore, the superiority of the proposed approach in comparison with the sequential approach as well as each of the other beamforming and deconvolution approaches alone is also shown. Results demonstrate that our approach combines the advantages of both methods and offers ultrasound images with superior resolution and contrast.
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In order to improve the acoustic source identification performance of beamforming when ground reflection exists, an array point spread function was derived and a corresponding non-negative least squares deconvolution method was given for a mirror-ground beamforming method. Simulations of a known acoustic source indicate that the given method is correct, and it could not only clear the acoustic source identification results effectively by improving the spatial resolution and attenuating the sidelobe interference, but it could also be superior to the conventional beamforming deconvolution method. On this basis, experiments were conducted to validate the correctness of the simulations and the effectiveness of the mirror-ground beamforming deconvolution method in practical application.
Least-squares function approximation
Point spread function
Reflection
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The localization of sound sources with delay-and-sum (DAS) beamforming is limited by a poor spatial resolution-particularly at low frequencies. Various methods based on deconvolution are examined to improve the resolution of the beamforming map, which can be modeled by a convolution of the unknown acoustic source distribution and the beamformer's response to a point source, i.e., point-spread function. A significant limitation of deconvolution is, however, an additional computational effort compared to beamforming. In this paper, computationally efficient deconvolution algorithms are examined with computer simulations and experimental data. Specifically, the deconvolution problem is solved with a fast gradient projection method called Fast Iterative Shrikage-Thresholding Algorithm (FISTA), and compared with a Fourier-based non-negative least squares algorithm. The results indicate that FISTA tends to provide an improved spatial resolution and is up to 30% faster and more robust to noise. In the spirit of reproducible research, the source code is available online.
Convolution (computer science)
Wiener deconvolution
Least-squares function approximation
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An automatic focus map extraction method is presented that uses a modification of blind deconvolution for estimation of localized blurring functions. We use these local blurring functions [so-called point-spread functions (PSFs)] for extraction of focus areas on ordinary images. In this inverse task our goal is not image reconstruction but the estimation of localized PSFs and the relative focus map. Thus the method is less sensitive than general deconvolution is to noise and ill-posed deconvolution problems. The focus areas can be estimated without any knowledge of the shooting conditions or of the optical system used.
Point spread function
Wiener deconvolution
Optical transfer function
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Wiener deconvolution
Point spread function
Reflector (photography)
Depth of field
Image formation
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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.
Wiener deconvolution
Speckle noise
Point spread function
SIGNAL (programming language)
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We present an application of an iterative deconvolution algorithm to speckle interferometric data. This blind deconvolution algorithm permits the recovery of the target distribution when the point spread function is either unknown or poorly known. The algorithm is applied to specklegrams of the multiple star systems, and the results for (zetz) UMa are compared to shift-and-add results for the same data. The linearity of the algorithm is demonstrated and the signal-to-noise ratio of the reconstruction is shown to grow as the square root of the number of specklegrams used. This algorithm does not require the use of an unresolved target for point spread function calibration.
Point spread function
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When utilizing conventional regular focus point distributions to define a relatively large source region, Fourier-based deconvolution beamforming, an attractive acoustic source identification technique, would suffer from some limitations: 1) significantly deteriorative location and quantification accuracy for sources away from the center of the focus region; 2) pronounced sidelobe contaminations. The arch-criminal is the assumption that the point spread function (PSF) is definitely shift-invariant over the entire focus region fails to be satisfied well. This paper focuses on remedying these limitations for two-dimensional (2D) acoustic source identification. First and foremost, a novel focus point generation approach is introduced, which can generate unconventional irregular 2D focus point distributions tending to make PSF more shift-invariant. Additionally, a sidelobe suppression approach is suggested. Effects of these approaches are examined both with computer simulations and experimentally. This study provides the feasibility of using Fourier-based deconvolution beamforming to accurately and efficiently identify acoustic sources in a relatively large region.
Point source
Identification
Point spread function
Cite
Citations (5)