Ultrasound Image Deconvolution Using Fundamental and Harmonic Images

Ultrasound (US) image restoration from radio frequency (RF) signals is generally addressed by deconvolution techniques mitigating the effect of system point spread function (PSF). Most existing methods estimate tissue reflectivity (TRF) so-called fundamental US images, based on an model assuming linear wave propagation. However, several human tissues or with contrast agents have a nonlinear behavior when interacting waves leading to harmonic images. This work takes this nonlinearity into account in context TRF restoration, considering both and RF signals. Starting two observation models (for images), estimation expressed as minimization cost defined sum data fidelity terms one sparsity-based regularization stabilizing solution. The high attenuation depth echoes integrated direct that relates observed TRF. interest proposed method shown through synthetic vivo results compared other methods.