A simple measurement matrix for compressed sensing of synthetic aperture ultrasound imaging
by Linhong Wang; Lu Kong; Ping Wang
International Journal of Embedded Systems (IJES), Vol. 15, No. 3, 2022

Abstract: Due to the increasing of sampling frequency in synthetic aperture ultrasound imaging, huge data and high system implementation complexity will be caused by using Nyquist sampling. To solve this problem, compressed sensing (CS) is applied to synthetic aperture ultrasound imaging. As an important component of CS, the measurement matrix is not only the key to ensure the quality of the reconstructed signal, but also determines the difficulty of hardware implementation. However, the random measurement matrix is difficult to implement in hardware systems, so a deterministic measurement matrix - binary sparse block diagonal matrix (BSBD) which is suitable for synthetic aperture ultrasound imaging with a simple structure is proposed in this paper. The BSBD matrix is made up of '0' except for one or more '1' in each column. The simulations of scattering point targets, cyst phantoms, and geabr_0 data were conducted by Field II. The simulation results indicate that the proposed measurement matrix has the smallest reconstruction error and the highest peak signal to noise ratio when reconstructing ultrasound signals, and can reconstruct ultrasound images with high accuracy at 30% sampling data.

Online publication date: Thu, 11-Aug-2022

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