Ultrasound Raw Radio Frequency Signal Reconstruction Based on Sparse Fast Fourier Transform

This paper presents a new scheme for reconstructing raw radio frequency (RF) signals received from ultrasound transducer elements. The scheme is based on sparse fast Fourier transform (SFFT), which can reconstruct a signal that is sparse in the frequency domain compared to the samples required by the Shannon-Nyquist sampling theorem. A cyst phantom and line array probe with 96 activated elements in the Field II software was applied in our simulation. When the energy of the largest k frequency coefficients was 90% of the total energy and 5% of the candidate frequency coefficients were utilized in the SFFT algorithm, we could recover the raw RF signals with a mean absolute error (MAE) of 0.0049. The MAE of the scan lines beamformed from the reconstructed raw RF signals was 0.0040. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the resulting ultrasound image were 38.34 dB and 99.12%, respectively. The simulation results show that the SFFT algorithm can reconstruct a raw RF signal with high precision and attain higher image quality than the interpolation beamforming method.