USSR: An ultrasound sparse regularization framework
In pulse-echo ultrasound (US) imaging, recovering the tissue response from the element-raw data can be formulated as an ill-posed inverse problem. Standard methods rely on the delay-and-sum (DAS) which gives a fast, but rough, estimated solution of the problem. An alternative consists in using regularization approaches, which exploit a formulation of a measurement model and additional prior knowledge to leverage the ill-posedness of the problem. Among the regularization methods, sparse regularization has recently grown many interest [Besson et al. UFFC16, David et al. JASA15, Szasz et al. UFFC16]. However current methods require a tremendous amount of memory and are unachievable in realistic US imaging applications.