A Total Variation Regularizer With Partially Known Support for Pulse-Echo Speed-of-Sound Imaging.

Pulse-echo speed-of-sound (SoS) imaging is emerging as a powerful extension of conventional echography. It shows particular promise for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD), whose prevalence is increasing worldwide alongside obesity rates. However, current techniques often lack robustness, particularly when limited data are available. Existing methods rely on different regularization strategies to incorporate knowledge on the SoS distribution within tissues. In this work, we propose a novel regularization approach that automatically integrates information from the B-mode image. The proposed regularizer relies on the principle of sparsity with partially known support applied to a total variation (TV) norm, enabling increased robustness compared to regularizers disregarding information contained in B-mode images, or circumventing drawbacks of approaches based on the manual segmentation of the latter. We evaluate the proposed technique with simulated and in vivo data, considering both large and reduced datasets, with a specific focus on liver imaging. The results indicate that our method enhances SoS accuracy with simulated data when compared with regularizers proposed in the literature, whereas the robustness to data reduction is improved with in vivo data. Notably, our approach can facilitate the deployment of pulse-echo SoS imaging on ultraportable transducers, which are typically constrained in data acquisition capacity.