Enhancing single-element compressive ultrasound imaging through novel random aperture masking and data mixing strategy

As ultrasound techniques continue to evolve, the integration of compressed sensing technology has emerged as a pivotal advancement, offering a transformative impact on the landscape of ultrasound imaging. A key attribute of compressed sensing lies in its ability to facilitate a substantial reduction in both machinery size and power consumption. This technological synergy not only addresses crucial practical considerations in the design of ultrasound systems but also opens avenues for enhanced portability and energy efficiency. This study develops a model and introduces an aperture mask with a mixing scheme for compressive ultrasound imaging employing a single transducer, aiming to minimize the loss of information and scrutinize the variables influencing image quality while facilitating computationally efficient system simulation. A detailed procedural guide is presented for generating synthetic data, accompanied by qualitative and quantitative analyses using several sparse recovery methods under different experimental conditions. This study's analysis reveals that the proposed strategy achieves improved metrics, offering advantages for sparse recovery. Specifically, the finite element results demonstrate approximately a 10% improvement in the condition number of the measurement matrix, reflecting enhanced numerical stability.