Attenuation Imaging with Pulse-Echo Ultrasound Based on an Acoustic Reflector

Rau, Richard; Unal, Ozan; Schweizer, Dieter; Vishnevskiy, Valery; Göksel, Orçun

doi:10.1007/978-3-030-32254-0_67

Cited by 16 publications

(19 citation statements)

References 20 publications

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“…This then results in artifactually higher UA values outside the axial edges at higher frequencies, thus resulting in increased y values. Such axial elongation of inclusions is a known limitation of imaging due to missing lateral projections, as also reported and discussed in [28,30].…”

Section: Simulation Experimentsmentioning

confidence: 57%

“…Furthermore, due to reconstruction instabilities, only synthetic and phantom examples could be quantified, but no actual tissue samples. Recently, we proposed a novel reconstruction approach for arbitrary spatial UA distributions in [28] based on limited-angle computed tomography (LA-CT) with a conventional linear-array transducer and a passive reflector. We herein analyze this method in depth and extend it to additionally characterize the local frequencydependence of UA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Rau¹,

Unal²,

Schweizer³

et al. 2020

Preprint

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Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequencydependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, from images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as help attenuation compensation other ultrasound-based imaging techniques.Keywords ultrasound • attenuation • speed of sound • computed tomography • limited angle tomography

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Section: Simulation Experimentsmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Rau¹,

Unal²,

Schweizer³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, reflection boundaries could be detected and removed using a simple phase symmetry (PS) algorithm [38], which is designed to estimate directional reflections at tissue boundaries such as bone surfaces. In a way similar to estimating and compensating for reflections, acoustic attenuation can indeed also be reconstructed a-priori [39] in order to spatially normalize incident acoustic energy to decouple its effect from our reconstructed scatterer amplitudes.…”

Section: Discussionmentioning

confidence: 99%

Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

Zhang

Vishnevskiy

Göksel

2020

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

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Simulation-based ultrasound training can be an essential educational tool. Realistic ultrasound image appearance with typical speckle texture can be modeled as convolution of a point spread function with point scatterers representing tissue microstructure. Such scatterer distribution, however, is in general not known and its estimation for a given tissue type is fundamentally an ill-posed inverse problem. In this paper, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed ultrasound data. We herein propose to impose a known statistical distribution on scatterers and learn the mapping between ultrasound image and distribution parameter map by training a convolutional neural network on synthetic images. In comparison with several existing approaches, we demonstrate in numerical simulations and with in-vivo images that the synthesized images from scatterer representations estimated with our approach closely match the observations with varying acquisition parameters such as compression and rotation of the imaged domain.

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“…where λ is the weight parameter of regularization required to help robustly solve these ill-posed problems. Similarly to [22], [25], [32], [34], we herein use l 1 -norm for both the data and regularization terms for robustness to outliers in, respectively, the measurements and the reconstructions [44]. Due to a lack of full angular coverage of measurements, regularization matrix D implements an LA-CT specific image filtering to suppress streaking artifacts orthogonal to missing projections via anisotropic weighting of directional gradients [45].…”

Section: Reconstruction Of Phase Velocity and Attenuationmentioning

confidence: 99%

Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror

Chintada,

Rau,

Goksel

2022

Preprint

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Speed-of-sound and attenuation of ultrasound waves vary in the tissues. There exist methods in the literature that allow for spatially reconstructing the distribution of group speedof-sound (SoS) and frequency-dependent ultrasound attenuation (UA) using reflections from an acoustic mirror positioned at a known distance from the transducer. These methods utilize a conventional ultrasound transducer operating in pulse-echo mode and a calibration protocol with measurements in water. In this study, we introduce a novel method for reconstructing local SoS and UA maps as a function of acoustic frequency through Fourier-domain analysis and by fitting linear and power-law dependency models in closed form. Frequency-dependent SoS and UA together characterize the tissue comprehensively in spectral domain within the utilized transducer bandwidth. In simulations, our proposed methods are shown to yield low reconstruction error: 0.01 dB/cm•MHz y for attenuation coefficient and 0.05 for the frequency exponent. For tissue-mimicking phantoms and exvivo bovine muscle samples, a high reconstruction contrast was achieved. Attenuation exponents in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 0.6 on average. Linear dispersion of SoS in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 4.0 m/s•MHz on average. These findings were reproducible when the inclusion and substrate materials were exchanged. Bulk loss modulus in the bovine muscle sample was computed to be approximately 4 times the bulk loss modulus in the gelatin-cellulose mixture. Such frequency-dependent characteristics of SoS and UA, and bulk loss modulus may therefore differentiate tissues as potential diagnostic biomarkers.

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Attenuation Imaging with Pulse-Echo Ultrasound Based on an Acoustic Reflector

Cited by 16 publications

References 20 publications

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror

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