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

Abstract

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.

Non-contrast power Doppler ultrasound imaging for early assessment of trans-arterial chemoembolization of liver tumors

“…In this work, the full 1 s ensemble size was beamformed in the time-domain on a single CPU and was the most computationally expensive step. However, apart from implementing a more parallelized approach using smaller ensemble sizes, GPU and Fourier-based methods 39–41 have been shown to be able to facilitate synthetic aperture beamforming in real-time. Additionally, GPU-based autocorrelation methods have been proposed for real-time motion estimation 42 which could allow for real-time implementations of adaptive demodulation.…”

Deep Learning in Ultrasound Imaging

“…Instead of Nyquist-rate sampling of pre-beamformed and multiplexed channel data, compressive sub-Nyquist sampling methods permit reduced-rate imaging without sacrificing quality [3], [19]. After (reduced-rate) digitization, additional compression may be achieved through neural networks that serve as application-specific encoders.…”

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

“…The randomizations of these degrees of freedom generate sensing matrices (3) with random structures that potentially improve the aforementioned characteristic measures and, consequently, aid in meeting the associated sufficient conditions. In fact, the degrees of freedom in the physical models underlying magnetic resonance imaging (MRI) [35,11,12], [41] and compressed beamforming in UI [70]- [74], for example, specify subsets of scaled Fourier coefficients to be processed. Their random and uniform selection generates the aforementioned random sensing matrix meeting the RIP with relatively few observations.…”

“…In the simulation study, for instance, the approximate Gaussian distribution of the energy in the Fourier coefficients (8b) over the frequency axis (cf . Table IV) Using sub-Nyquist temporal sampling rates, the proposed method resembles the compressed beamforming methods [70]- [74]. In fact, both types of methods leverage a sparsitypromoting ℓ q -minimization method (P q,η ) to recover specific signals from only a few Fourier coefficients (8b) associated with the selected discrete frequencies.…”

“…They model the one-dimensional focused signals composing the image as quantized finite streams of known pulses 10 and individually recover hundreds of nearly-sparse parameter vectors defining these streams. They primarily reduce the temporal sampling rates, and only their most recent versions additionally support high frame rates [70], [71]. The superior physical model driving the proposed method, however, further increases the frame rate and significantly improves the image quality.…”