Compressed Sensing framework for Limited-element Compounded Diverging Waves: Initial Results

Compressed Sensing (CS) framework was reported previously to reduce the number of active receive elements in diverging beam-based multi-element synthetic transmit aperture imaging (MSTA). One major issue in that approach was that the active receive elements need to be selected randomly for every transmission. Recently, a methodology for an optimal selection of active receive elements once and using it for all the transmission of MSTA was shown using a genetic algorithm (GA). However, the number of physical transducer elements cannot be reduced due to the requirement of linear electronic scanning of the transmit beam. To overcome this limitation, the CS framework is adopted in this work for a limited-element compounded diverging wave (LeCDW), where the same set of sparse elements are used for all the transmissions and receptions. The simulation dataset for this study was obtained from Field II. The obtained results suggest that it is possible to reduce the number of receive elements by 50% without significant degradation in the image quality. These preliminary results indicated that it is possible to realize the CS framework using a customized transducer with physical elements at random locations.