Global Speed-of-Sound Prediction Using Transmission Geometry

Bezek, Can Deniz; Bilgin, Mert; Zhang, Lin; Göksel, Orçun

doi:10.1109/ius54386.2022.9958762

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Beamforming SoS may largely affect the local SoS reconstruction results, as also demonstrated in [15]. Accordingly, we herein employ an iterative approach for finetuning the beamforming SoS, using the median reconstruction SoS for beamforming in the next iteration.…”

Section: Discussionmentioning

confidence: 94%

“…Estimation of a global SoS value in the imaged tissue or layer-wise approximations thereof have been studied by several researchers, especially in the context of correcting aberrations due to SoS [4]- [7]. Several global SoS estimation methods employ trial-and-error to optimize some quality-metric mostly based on a form of speckle analysis [8]- [11] or phase variance [12], [13], although model-based methods have also been proposed [14], [15]. In contrast, for reconstructing maps of local SoS variation, different mechanical setups have been proposed including transmission-mode [16]- [21], hand-held reflector-based [22], and reflector-free pulse-echo mode [23], [24] systems and methods.…”

Section: Introductionmentioning

confidence: 99%

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Robust Imaging of Speed-of-Sound Using Virtual Source Transmission

Schweizer¹,

Rau²,

Bezek³

et al. 2023

Preprint

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Speed-of-sound (SoS) is a novel imaging biomarker for assessing biomechanical characteristics of soft tissues. SoS imaging in pulse-echo mode using conventional ultrasound systems with hand-held transducers has the potential to enable new clinical uses. Recent work demonstrated diverging waves from single-element (SE) transmits to outperform plane-wave sequences. However, single-element transmits have severely limited power and hence produce low signal-to-noise ratio (SNR) in echo data. We herein propose Walsh-Hadamard (WH) coded and virtual-source (VS) transmit sequences for improved SNR in SoS imaging. We additionally present an iterative method of estimating beamforming SoS in the medium, which otherwise confound SoS reconstructions due to beamforming inaccuracies in the images used for reconstruction. Through numerical simulations, phantom experiments, and in-vivo imaging data, we show that WH is not robust against motion, which is often unavoidable in clinical imaging scenarios. Our proposed virtual-source sequence is shown to provide the highest SoS reconstruction performance, especially robust to motion-artifacts. In phantom experiments, despite having a comparable SoS rootmean-square-error (RMSE) of 17.5 to 18.0 m/s at rest, with a minor axial probe motion of ≈0.67 mm/s the RMSE for SE, WH, and VS already deteriorate to 20.2, 105.4, 19.0 m/s, respectively; showing that WH produces unacceptable results, not robust to motion. In the clinical data, the high SNR and motion-resilience of VS sequence is seen to yield superior contrast compared to SE and WH sequences.

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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Robust Imaging of Speed-of-Sound Using Virtual Source Transmission

Schweizer¹,

Rau²,

Bezek³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Several theoretical studies suggest the suitability of convolutional operations in characterizing SoS-related time delays [35], [36]. The convolutional neural network is a type of network architecture designed upon convolution operations and has proven successful in ultrasound imaging [37]- [39].…”

Section: Neural Networkmentioning

confidence: 99%

Robust Deep Learning for Pulse-echo Speed of Sound Imaging via Time-shift Maps

Chen,

Han

2024

Preprint

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Accurately imaging the spatial distribution of longitudinal sound speed has a profound impact on image quality and the diagnostic value of ultrasound. Knowledge of sound speed distribution allows effective aberration correction to improve the image quality. Sound speed imaging also provides a new mechanism of imaging contrast that will facilitate disease diagnosis. However, sound speed imaging is challenging in the pulse-echo mode. Deep learning (DL) is a promising approach for pulse-echo speed of sound imaging, which may yield more accurate results than pure physics-based approaches. However, it often requires a large amount of training data and has limitations in model generalizability. To address these issues, we developed a physics-guided DL approach to image the spatial distribution of sound speed using pulse-echo data acquired from multiple angles. Instead of using the raw echo signals as the input to the DL model, we transformed the echo signals into time-shift maps based on local phase shifts between beamformed images. Using this approach, our model was shown to be generalizable when trained and tested using different scan settings in simulation studies. Furthermore, the simulation-trained model successfully reconstructed the sound speed maps of phantoms using experimental data. Compared with a state-of-the-art physics-based inversion approach, our approach reduced median root mean squared error (RMSE) by 26% and improved contrast-to-noise ratio (CNR) by 52% in the phantom experiment. These results demonstrated the accuracy and robustness of our approach.

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“…To overcome this limitation, pulse-echo SoS estimation methods have been developed to enable SoS estimation in vivo without the need for bilateral access to the tissue. These pulse-echo SoS estimation methods are based on principles such as beam tracking [31], speckle brightness maximization [32], spatial coherence [11], [12], geometric model fitting [33], or image registration for steered transmissions [34], [35]. However, few of these SoS estimation techniques have been demonstrated in real-time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Speed-of-Sound Estimation In Vivo via Steered Plane Wave Ultrasound

Xiao,

Torre,

2024

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

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Speed-of-sound (SoS) is an intrinsic acoustic property of human tissues and has been regarded as a potential biomarker of tissue health. To foster the clinical use of this emerging biomarker in medical diagnostics, it is important for SoS estimates to be derived and displayed in real-time. Here, we demonstrate that concurrent global SoS estimation and B-mode imaging can be achieved live on a portable ultrasound scanner. Our innovation is hinged upon the design of a novel pulse-echo SoS estimation framework that is based on steered plane wave imaging. It has accounted for the effects of refraction and imaging depth when the medium SoS differs from the nominal value of 1540 m/s that is conventionally used in medical imaging. The accuracy of our SoS estimation framework was comparatively analyzed with through-transmit timeof-flight measurements in vitro on 15 custom agar phantoms with different SoS values (1508 to 1682 m/s) and in vivo on human calf muscles (N = 9; SoS range: 1560 to 1586 m/s). Our SoS estimation framework has a mean signed difference of, respectively, -0.6±2.3 m/s in vitro and -2.2±11.2 m/s in vivo relative to the reference measurements. Additionally, our real-time system prototype has yielded simultaneous SoS estimates and B-mode imaging at an average frame rate of 18.1 fps. Overall, by realizing real-time tissue SoS estimation with B-mode imaging, our innovation can foster the use of tissue SoS as a biomarker in medical ultrasound diagnostics.

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Global Speed-of-Sound Prediction Using Transmission Geometry

Cited by 5 publications

References 11 publications

Robust Imaging of Speed-of-Sound Using Virtual Source Transmission

Robust Imaging of Speed-of-Sound Using Virtual Source Transmission

Robust Deep Learning for Pulse-echo Speed of Sound Imaging via Time-shift Maps

Real-Time Speed-of-Sound Estimation In Vivo via Steered Plane Wave Ultrasound

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