FoCUS: Fourier-Based Coded Ultrasound

Lahav, Almog; Chernyakova, Tanya; Eldar, Yonina C.

doi:10.1109/tuffc.2017.2760359

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…avoids the oversampling dictated by digital implementation of beamforming in time. Furthermore, when assuming that the beam obeys a FRI model, the received signals can be sampled at sub-Nyquist rates, leading to up to 28 fold reduction in sampling rate [19], [20], [21].…”

Section: Analog Processingmentioning

confidence: 99%

Deep Learning in Ultrasound Imaging

2020

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Deep learning is taking an ever more prominent role in medical imaging. This paper discusses applications of this powerful approach in ultrasound imaging systems along with domain-specific opportunities and challenges.ABSTRACT | We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage.As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

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Section: Analog Processingmentioning

confidence: 99%

Deep Learning in Ultrasound Imaging

2020

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“…Takeuchi 19 uses the coded excitation for medical ultrasound imaging. Encoding with chirp signals, [20][21][22][23] Barker codes, 24 m-sequences, 25,26 and complementary Golay sequences (CGSs), 27,28 have been studied. Some researchers used both chirp signals and CGSs to make a performance comparison.…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio of diverging waves in multiscattering media: Effects of signal duration and divergence angle

Kumru

Köymen

2022

The Journal of the Acoustical Society of America

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In this paper, SNR maximization in coded diverging waves is studied, and experimental verification of the results is presented. Complementary Golay sequences and binary phase shift keying modulation are used to code the transmitted signal. The SNR in speckle and pin targets is maximized with respect to chip signal length. The maximum SNR is obtained in diverging wave transmission when the chip signal is as short a duration as the array permits. We determined the optimum diverging wave profile to confine the transmitted ultrasound energy in the imaging sector. The optimized profile also contributes to the SNR maximization. The SNR performances of the optimized coded diverging wave and conventional single-focused phased array imaging are compared on a single frame basis. The SNR of the optimized coded diverging wave is higher than that of the conventional single-focused phased array imaging at all depths and regions.

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Fourier-Domain Beamforming and Structure-Based Reconstruction for Plane-Wave Imaging

Chernyakova

Cohen

Mulayoff

et al. 2018

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

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Ultrafast imaging based on coherent plane-wave compounding is one of the most important recent developments in medical ultrasound. It significantly improves the image quality and allows for much faster image acquisition. This technique, however, requires large computational load motivating methods for sampling and processing rate reduction. In this work, we extend the recently proposed frequency-domain beamforming (FDBF) framework to plane-wave imaging. Beamforming in frequency yields the same image quality while using fewer samples. It achieves at least fourfold sampling and processing rate reduction by avoiding oversampling required by standard processing. To further reduce the rate, we exploit the structure of the beamformed signal and use compressed sensing methods to recover the beamformed signal from its partial frequency data obtained at a sub-Nyquist rate. Our approach obtains tenfold rate reduction compared with standard time-domain processing. We verify performance in terms of spatial resolution and contrast based on the scans of a tissue mimicking the phantom obtained by a commercial Aixplorer system. In addition, in vivo carotid and thyroid scans processed using standard beamforming and FDBF are presented for qualitative evaluation and visual comparison. Finally, we demonstrate the use of FDBF for shear-wave elastography by generating velocity maps from the beamformed data processed at sub-Nyquist rates.

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FoCUS: Fourier-Based Coded Ultrasound

Cited by 4 publications

References 31 publications

Deep Learning in Ultrasound Imaging

Deep Learning in Ultrasound Imaging

Signal-to-noise ratio of diverging waves in multiscattering media: Effects of signal duration and divergence angle

Fourier-Domain Beamforming and Structure-Based Reconstruction for Plane-Wave Imaging

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