Abstract: Accurate tissue-clutter rejection and blood flow estimation remain challenging in ultrasound imaging. Traditionally, this estimation is performed by assuming static tissues. Only a few preprocessing techniques attempt to deal with the more realistic but challenging scenario where the tissues are moving. This paper tackles this scenario and presents a novel method for computing blood flow from moving tissues in ultrafast ultrasound imaging. The proposed computational ultrasound imaging method solves a global inverse problem that jointly computes blood flow, tissues, and their motions. The resulting cost function incorporates each component specificity using appropriate regularizations and is fully convex. The cost function is minimized using an alternating proximal-forward-backward algorithm with convergence guarantees. Furthermore, the proposed method is integrated into a multi-resolution scheme for large motions. The experiments show that the proposed method accurately compensates tissue motions, improving the precision of blood flow computation compared to previous methods. Experiments on in vivo images demonstrate the effectiveness of the proposed method in realistic scenarios with large motions.
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