Ultrasound Signal Reconstruction from Sparse Samples Using a Low-Rank and Joint-Sparse Model

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage can become a bottle neck in ultrasound system design. To reduce the amount of sampled channel data, several strategies based on compressive sensing (CS) have been proposed. However, the reconstruction accuracy of CS-based methods is highly dependent on the sparse basis and the number of measurements for each channel cannot be lower than the sparsity thereby limiting the data reduction rate. Therefore, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is better adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.