Reconstruction of Ultrasound RF Echoes Modeled as Stable Random Variables

Achim, Alin; Basarab, Adrian; Tzagkarakis, George; Tsakalides, Panagiotis; Kouamé, Denis

doi:10.1109/tci.2015.2463257

Cited by 18 publications

(17 citation statements)

References 35 publications

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“…IRLS [13,16], D-AMP [18] and the proposed AMP-based reconstruction algorithm using six combinations of image representations and denoisers are evaluated on an US image acquired with a clinical scanner (Sonoline Elegra) that was modified for research and a 7.5-MHz linear probe (Siemens Medical Systems, Issaquah, WA, USA), giving access to RF data sampled at 50 MHz. Also, the algorithms were implemented on HP ENVY running a 2.6GHz Intel(R) CoreTM i7-6500C processor with 8GB RAM under the Matlab R2014a environment.…”

Section: Reconstruction Resultsmentioning

confidence: 99%

“…The first one is similar to (2), but uses a more general l p optimization problem solved with the iteratively reweighted least squares (IRLS) algorithm [13]. Based on the assumption that US signals follow an α-stable distribution [14,15], the method in [16] uses the characteristic exponent α , calculated by fitting an α-stable distribution to the DCT of US images, to estimate an optimal value of p required for l p optimization, i.e. p = α − 0.01.…”

Section: Comparative Methodsmentioning

confidence: 99%

“…p = α − 0.01. This way of choosing p has been shown to lead to better US reconstruction performance compared to standard basis pursuit and orthogonal matching algorithms [16,17]. D-AMP equipped with BM3D as a denoiser, shown in [18] to enhance CS reconstruction performance even for nonsparse images, was the second method used for comparison.…”

Section: Comparative Methodsmentioning

confidence: 99%

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Ultrasound image reconstruction from compressed measurements using approximate message passing

Kim

Basarab

Hill

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17008The contribution was presented at EUSIPCO 2016 :http://www.eusipco2016.org/ ABSTRACTIn this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. We compare the results with existing reconstruction algorithms already used in ultrasound imaging and quantify the performance improvement.

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Section: Reconstruction Resultsmentioning

confidence: 99%

Section: Comparative Methodsmentioning

confidence: 99%

Section: Comparative Methodsmentioning

confidence: 99%

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Ultrasound image reconstruction from compressed measurements using approximate message passing

Kim

Basarab

Hill

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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“…The reweighted ℓ 1 minimization [99] is a special instance of Foucart's algorithm for q = 0 that does not unequivocally achieve the best results. Achim et al [100] proposed a method to infer the optimal parameter q from the characteristic exponent of a symmetric α-stable distribution modeling the temporal samples of an individual beamformed RF voltage signal or its DFTs. The author speculates that suitable statistical models for the normalized nearly-sparse representation (40) could enable similar methods.…”

Section: B Image Recovery By ℓ Q -Minimization Is Quantitativementioning

confidence: 99%

Notice of Removal: Random incident sound waves for fast compressed pulse-echo ultrasound imaging

Schiffner

Schmitz

2017

2017 IEEE International Ultrasonics Symposium (IUS)

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Established image recovery methods in fast ultrasound imaging, e.g. delay-and-sum, trade the image quality for the high frame rate. Cutting-edge inverse scattering methods based on compressed sensing (CS) disrupt this tradeoff via a priori information. They iteratively recover a high-quality image from only a few sequential pulse-echo measurements or less echo signals, if (i) a known dictionary of structural building blocks represents the image almost sparsely, and (ii) their individual pulse echoes, which are predicted by a linear model, are sufficiently uncorrelated. The exclusive modeling of the incident waves as steered plane waves or cylindrical waves, however, has so far limited the convergence speed, the image quality, and the potential to meet condition (ii).Motivated by the benefits of randomness in CS, a novel method for the fast compressed acquisition and the subsequent recovery of images is proposed to overcome these limitations. It recovers the spatial compressibility fluctuations in weakly-scattering soft tissue structures, where an orthonormal basis meets condition (i), by a sparsity-promoting ℓq-minimization method, q ∈ [0; 1]. A realistic d-dimensional model, d ∈ {2, 3}, accounting for diffraction, single monopole scattering, the combination of powerlaw absorption and dispersion, and the specifications of a planar transducer array, predicts the pulse echoes of the individual basis functions. Three innovative types of incident waves, whose syntheses leverage random apodization weights, time delays, or combinations thereof, aid in meeting condition (ii).In two-dimensional numerical simulations, single realizations of these waves outperform the prevalent quasi-plane wave for both the canonical and the Fourier bases. They significantly decorrelate the pulse echoes, e.g. they reduce the full extents at half maximum of the point spread functions by up to 73.7 %. For a tissue-mimicking phantom and q ∈ {1, 0.5}, they improve the convergence speed and the image quality in terms of the mean structural similarity indices and the relative root meansquared errors by up to {2.7 %, 22.9 %}, {44.1 %, 55.5 %}, and {42 %, 60.5 %}, respectively.Index Terms-random incident waves, inverse scattering, compressed sensing, sparse regularization, ultrafast ultrasound imaging, computational ultrasound imaging, fast multipole method, GPU computing

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“…The log-cumulants of heavy-tailed α-stable distribution which exist for all orders are given as the following [8]:…”

Section: Htr Distributionmentioning

confidence: 99%

Non-Gaussian amplitude PDF modeling of ultrasound images based on a novel generalized Cauchy-Rayleigh mixture

Bibalan

Amindavar

2016

J Image Video Proc.

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In this paper, a mixture of generalized Cauchy distribution and Rayleigh distribution that possesses a closed-form expression is proposed for modeling the heavy-tailed Rayleigh (HTR) distribution. This new approach is developed for analytically modeling the amplitude distribution of ultrasound images based on the HTR distribution. HTR as a non-Gaussian distribution is basically the amplitude probability density function (PDF) of the complex isotropic symmetric α-stable (SαS) distribution which appears in the envelope distribution of ultrasonic images. Analytic expression for HTR distribution is a momentous consideration in signal processing with stable random variables. Furthermore, we introduce a mixture ratio estimator based on the energy of amplitude PDF which contains both α and γ parameters. For a quantitative assessment, we compare the accuracy and computational complexity of the proposed mixture with other approximations of HTR distribution through several numeral simulations on synthetic random samples. Experimental results obtained from the Kolmogorov-Smirnov (K-S) distance and Kullback-Leibler (K-L) divergence as the goodness-of-fit tests on real ultrasound images reveal the favor of the new mixture model.

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Reconstruction of Ultrasound RF Echoes Modeled as Stable Random Variables

Cited by 18 publications

References 35 publications

Ultrasound image reconstruction from compressed measurements using approximate message passing

Ultrasound image reconstruction from compressed measurements using approximate message passing

Notice of Removal: Random incident sound waves for fast compressed pulse-echo ultrasound imaging

Non-Gaussian amplitude PDF modeling of ultrasound images based on a novel generalized Cauchy-Rayleigh mixture

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