Structurally random Fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging

Abstract

Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a "stethoscope" while further processing and image construction can be done using cloud-based processors. Toward this goal, we use Structurally Random Matrices (SRM) for compressive sensing of ultrasound data, Fourier sparsifying matrix for recovery in 1D, and frequency domain approach for 2D ultrasound image reconstruction. This approach is demonstrated through wire phantom and in vivo carotid arteries data from ultrasound system using 25%, 12.5%, and 6.25% of the full data rate and ultrasound images of similar perceived quality quantified by Structural Similarity Index Metric (SSIM).

Image parallel block compressive sensing scheme using DFT measurement matrix

“…This performance mainly depends on the choosing of measurement matrix. In the exiting literatures, the conventional measurement matrices usually fall into three types or groups: Random measurement matrices, such as Gaussian matrices and Bernoulli matrices, chaos-based measurement matrix [45], and structurally random matrices (SRM) [13]. In image-based CS filed, random matrices are widely used as the measurement matrix due to its universality.…”