Compressive sensing for raw RF signals reconstruction in ultrasound

We address in this paper the feasibility of the recent compressive sensing (CS) technique for raw RF signals reconstruction in medical ultrasound. Successful CS reconstruction implies selecting a basis where the signal to be recovered has a sparse expansion. RF signals represent a specific challenge, because their oscillatory nature does not easily lend itself to a sparse representation. In that perspective, we propose to use the recently introduced directional wave atoms, which shows good properties for sparsely representing warped oscillatory patterns. Experiments were performed on simulated RF data from a cyst numerical phantom. CS reconstruction was applied to subsampled RF data, by removing 50% to 90% of the original samples. Reconstruction using wave atoms were compared to reconstruction obtained from Fourier and Daubechies wavelets. The obtained accuracies were in the range [0.4-4.0].10^-3, [0.2-2.8].10^-3 and [0.1-1.7].10^-3, using wavelets, Fourier and wave atoms respectively, showing the superiority of the wave atoms representation, and the feasibility of CS for the reconstruction of US RF data.