Blind deconvolution of ultrasonic traces accounting for pulse variance

Kaaresen, Kjetil F.; Bølviken, Erik

doi:10.1109/58.764843

Cited by 50 publications

(34 citation statements)

References 22 publications

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“…Deconvolution can be divided into two minimization problems: i) "pure" deconvolution where signal is treated as convolution of test object reflectivity function with probing (reference) signal [10][11][12] and ii) reference signal production [18,19,23]. Deconvolution is the optimization problem that uses sparse deconvolution methods such as matching pursuit, Prony model or orthogonal matching pursuit in order to deconvolve the mixed data [5,6,15,16].…”

Section: Estimation Of the Parameters Of The Approximating Functionsmentioning

confidence: 99%

“…Together with noise present, this significantly degrades the results of deconvolution. Blind deconvolution [17,18] is offering a production of reference signal using some simple mathematical model function, Gabor (harmonic oscillation with Gaussian envelope) being the most frequently used [6,19]. Result of such deconvolution significantly improves the SNR, adapts the signal spectral content changes and significantly reduces the required amount of computer memory needed to store or transmit the acquired data [20] by compressive sensing.…”

Section: Introductionmentioning

confidence: 99%

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Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Lukoševičiūtė¹,

Drulyte²,

Svilainis³

2016

J. vibroeng.

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Abstract.Comparison of the approximation performance of the ultrasonic reflections by simple model functions used in blind deconvolution is presented. New model function was proposed where causality of the signal is easily modelled without the need for piecewise-linear equations. Parameter estimation technique was developed. Performance investigation was done for three model functions using four experimentally collected ultrasonic signals under different signal filtering conditions.

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Section: Estimation Of the Parameters Of The Approximating Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Lukoševičiūtė¹,

Drulyte²,

Svilainis³

2016

J. vibroeng.

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show abstract

“…superimposed on a scanty structure of a few strong specular reflectors (e.g., liver arterioles, organ boundaries, etc. ), the corresponding tissue reflectivity function is likely to be a sparse sequence [28]. Such a behavior of the reflectivity function can be effectively described using a Laplacian distribution, as it has long been done in numerous applications in signal processing [29]- [32].…”

Section: B Statistical Modeliing Of the Reflectivity Functionmentioning

confidence: 99%

“…As a quality measure, we will use the normalized mean squared error (NMSE) criterion defined as (28) where y and denote the desired quantity and its estimate, respectively. In this paper, the expectation in (28) has been estimated based on the results of 200 independent trials.…”

Section: A In Silico Experimentsmentioning

confidence: 99%

Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

Michailovich

Tannenbaum

2007

IEEE Trans. on Image Process.

127

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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 deconvolution 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.

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“…Later a more flexible linear algebra approach involving finite-impulse response matrices was introduced [5]. This approach has been used in ultrasonic applications for deconvolving A-scans acquired from layered structures [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Sparse Deconvolution of B-Scan Images

Olofsson

Wennerström

2007

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Abstract-In this paper, a new computationally efficient sparse deconvolution algorithm for the use on B-scan images from objects with relatively few scattering targets is presented. It is based on a linear image formation model that has been used earlier in connection with linear minimum mean squared error (MMSE) two-dimensional (2-D) deconvolution. The MMSE deconvolution results have shown improved resolution compared to synthetic aperture focusing technique (SAFT), but at the cost of increased computation time. The proposed algorithm uses the sparsity of the image, reducing the degrees of freedom in the reconstruction problem, to reduce the computation time and to improve the resolution. The dominating task in the algorithm consists in detecting the set of active scattering targets, which is done by iterating between one up-dating pass that detects new points to include in the set, and a down-dating pass that removes redundant points. In the up-date, a spatiotemporal matched filter is used to isolate potential candidates. A subset of those are chosen using a detection criterion. The amplitudes of the detected scatterers are found by MMSE. The algorithm properties are illustrated using synthetic and real B-scan. The results show excellent resolution enhancement-and noise-suppression capabilities. The involved computation times are analyzed.

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Blind deconvolution of ultrasonic traces accounting for pulse variance

Cited by 50 publications

References 22 publications

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

Sparse Deconvolution of B-Scan Images

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