L1 And L2 Norm Depth-Regularized Estimation Of The Acoustic Attenuation And Backscatter Coefficients Using Dynamic Programming

Vajihi, Zara; Rosado-Méndez, Iván M.; Hall, Timothy J.; Rivaz, Hassan

doi:10.1109/isbi.2019.8759099

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…In this work, the authors exploited the depth-continuity of the tissue parameters in a regularized cost function. 2 and later, 1 regularization [29] was considered to enforce this continuity of the parameters of interest across the depth axis, where the latter was shown to exhibit superior performance. The resultant cost function was solved iteratively using DP and the method was shown to provide better estimates than those obtained by the least squares method in [26].…”

Section: Benchmark Methods For Comparisonmentioning

confidence: 99%

“…However, this method was proposed only for attenuation estimation. Estimation of both the backscatter and attenuation coefficients with 2 norm depth-regularization in [28] and later on with 1 norm regularization in [29] was performed using dynamic programming (DP) to solve the considered regularized least squares based cost function. While this DP based approach was demonstrated to perform better than the least squares method in [26], it might not be feasible for real-time applications due to the computational lag experienced while evaluating the cost function at each depth for all the possible values of the parameters within the user-specified search range.…”

Section: Introductionmentioning

confidence: 99%

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Fast linear least-squares method for ultrasound attenuation and backscatter estimation

et al. 2021

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Section: Benchmark Methods For Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

et al. 2021

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“…The work in [23] further proposed a spatially weighted TV regularization scheme to deal with tissue heterogeneity. Another method based on dynamic programming (DP) exploited piece-wise continuity of the target coefficients using an 2 [24] and 1 regularization [25] strategy. It was shown to provide more accurate estimates than [19].…”

Section: Introductionmentioning

confidence: 99%

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Birdi

D'hooge

Bertrand

2022

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

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Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, i.e., the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smoothness regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

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“…2 and 1 norm regularization schemes solved using dynamic programming-based approach [9,10]. An 2 regularized scheme, named ALGEBRA, was also recently proposed wherein the backscatter signal model was fit to the data in a linear least-squares (LLS) manner [11].…”

Section: Introductionmentioning

confidence: 99%

Spatially variant attenuation and backscatter coefficient estimation using a regularized linear least-squares approach

Birdi

D'hooge

Bertrand

2021

2021 IEEE International Ultrasonics Symposium (IUS)

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In the context of quantitative ultrasound, it is of importance to estimate the acoustic properties, primarily the attenuation and backscatter coefficients, of the tissue of interest. To this end, fast estimation techniques have recently been proposed in the literature, which fit a backscatter signal model to the data in a linear least-squares manner. The coefficient estimation problem becomes challenging when the underlying medium is heterogeneous. For such a spatially-variant/multilayered medium, the existing methods estimate the average attenuation coefficient until a given depth, from which local attenuation coefficients need to be extracted for attenuation mapping. Based on our recent work, we propose here a method which directly estimates the local attenuation coefficient values at each depth point. Unlike our previous work, the proposed method does not rely on prior-knowledge of the layer boundaries and use a regularization strategy to enforce smoothness of the target attenuation profile. It is shown to outperform the benchmark method, both on synthetic and phantom data. We further investigate spatial and contrast resolution of the proposed solution.

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L1 And L2 Norm Depth-Regularized Estimation Of The Acoustic Attenuation And Backscatter Coefficients Using Dynamic Programming

Cited by 12 publications

References 6 publications

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Spatially variant attenuation and backscatter coefficient estimation using a regularized linear least-squares approach

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