Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution to the image reconstruction problem. An alternative to DAS consists in using iterative techniques, which require both an accurate measurement model and a strong prior on the image under scrutiny. Toward this goal, much effort has been deployed in formulating models for US imaging, which usually require a large amount of memory to store the matrix coefficients. We present two different techniques, which take advantage of fast and matrix-free formulations derived for the measurement model and its adjoint, and rely on sparsity of US images in well-chosen models. Sparse regularization is used for enhanced image reconstruction. Compressed beamforming exploits the compressed sensing framework to restore high-quality images from fewer raw data than state-of-the-art approaches. Using simulated data and in vivo experimental acquisitions, we show that the proposed approach is three orders of magnitude faster than non-DAS state-of-the-art methods, with comparable or better image quality.

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct high-quality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality.

Ultrafast ultrasound imaging has become an intensive area of research thanks to its capability in reaching high frame rates. In this paper, we propose a scheme that allows the extension of the current Fourier-based techniques derived for planar acquisition to the reconstruction of sectorial scan with wide angle using diverging waves. The flexibility of the proposed formulation was assessed through two different Fourier-based techniques. The performance of the derived approaches was evaluated in terms of resolution and contrast from both simulations and in vitro experiments. The comparisons of the current state-of-the-art method with the conventional delay-and-sum technique illustrated the potential of the derived methods for producing competitive results with lower computational complexity.

Speed of sound estimation in ultrasound imaging is a growing modality with several clinical applications such as hepatic steatosis stages quantification. A key challenge for clinically relevant speed of sound estimation is to obtain repeatable values independent of superficial tissues and available in real-time. Recent works have demonstrated the feasibility to achieve quantitative estimations of the local speed of sound in layered media. However, such techniques require high computational power and exhibit instabilities. We present a novel speed of sound estimation technique based on an angular approach of ultrasound imaging in which plane waves are considered in transmit and receive. This change of paradigm allows us to rely on the refraction properties of plane waves to infer the local speed of sound values directly from the angular raw data. The proposed method robustly estimates the local speed of sound with only a few ultrasound emissions and with a low computational complexity which makes it compatible with real-time imaging. Simulations and in vitro experimental results show that the proposed method outperforms state-of-the-art approaches with biases and standard deviations lower than 10 m s−1, eight times fewer emissions, and 1000 times lower computational time. Further in vivo experiments validate its performance for liver imaging.

The theory of compressed sensing (CS) leverages upon structure of signals in order to reduce the number of samples needed to reconstruct a signal, compared to the Nyquist rate. Although CS approaches have been proposed for ultrasound (US) imaging with promising results, practical implementations are hard to achieve due to the impossibility to mimic random sampling on a US probe and to the high memory requirements of the measurement model. In this paper, we propose a CS framework for US imaging based on an easily implementable acquisition scheme and on a delay-and-sum measurement model.

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-sparse recovery algorithms, e.g. simultaneous normalized iterative hard thresholding, subspace greedy methods and subspace-augmented multiple signal classification (MUSIC) techniques. We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. We validate the proposed algorithms on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support. The supporting code is available on https://github.com/AdriBesson/joint_sparse_algorithms.