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.
|Number of pages||17|
|Journal||IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control|
|Early online date||1 Nov 2017|
|Publication status||Published - Mar 2018|
Besson, A., Perdios, D., Martinez, F., Chen, Z., Carrillo, R. E., Arditi, M., Wiaux, Y., & Thiran, J-P. (2018). Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control , 65(3), 339-355. https://doi.org/10.1109/TUFFC.2017.2768583