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Abstract-Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution of 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. Towards 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.

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Abstract-Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sparse Regularization framework which permits high-quality imaging at fast rates and with a very low memory footprint. The framework, based on highly parallelizable, parametric, matrix-free formulations of the measurement model and its adjoint as well as on well-chosen sparsity priors, is implemented on multi-threaded architectures and evaluated on the publicly available PICMUS dataset.

Sparse arrays are a topic of high interest within the ultrasound (US) imaging community, because of their promising ability to reduce costs, complexity, energy consumption, and data transfer requirements of US systems, thus addressing the main challenges of 3-D and portable 2-D systems. Undersampling a transducer array usually results in a significant increase in imaging artifacts, caused primarily by higher grating lobe (GL) levels. Thus, state-of-the-art sparse arrays design strategies focus on avoiding GLs, while compromising on the resulting image resolution and uniformity. In this work, we investigated the applicability of convolutional neural network (CNN)-based image reconstruction, having recently proven its potential in reducing GL artifacts, for reconstructing images from single unfocused acquisitions using uniformly undersampled linear array configurations on receive. The proposed reconstruction method consists of first computing a low-quality estimate from the undersampled single-shot acquisitions using a delay-and-sum (DAS) algorithm, followed by applying a real-time-capable CNN, trained specifically to reduce diffraction artifacts. Experiments were conducted within a simulation environment, in the context of plane wave imaging on a numerical test phantom dedicated to US image quality assessment. The proposed approach achieved an image comparable or better to that obtained from conventional DAS beamforming using the full array with uniformly undersampled arrays up to a factor of three, demonstrating a promising potential for sparse array imaging in general.