Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Affordable point-of-care ultrasound systems use Conventional Focused Beamforming (CFB) with limited number of active elements, which leads to reduction in the image quality. In addition, these systems have limited storage capacity. Therefore, reduction in the number of active elements and data in CFB without accompanying compromise in the image quality is highly desired. For data reduction, axial undersampling of the channel RF data and recovery of the fully-sampled RF data using Compressed Sensing (CS) has been proposed earlier. However, there is no detailed investigation on aperture undersampling and its recovery using CS framework, which leads to data reduction as well as reduction in the number of active receive elements. The central theme of this work is to investigate two sampling schemes to undersample the receive aperture (lateral undersampling) as well as axial undersampling of the selected channel RF data for CS framework. Experimental data for this study were acquired from a wire phantom and in-vitro cyst phantom using Sonix Touch \text{Q}+^{(\text{R})} ultrasound scanner. The results indicate that CS with Gaussian undersampling outperforms the uniform undersampling. In spite of discarding 90% of samples from the original RF frame data, B-mode images from CS framework had better LR and comparable contrast to that of reference image. Further, the results suggest that as the size of receive aperture from which the subset of channels are chosen increases, LR improves but contrast deteriorates. Thus, the results clearly demonstrate that it is possible to reduce the active channel count and data size using CS framework and obtain better-quality image compared to that obtained from corresponding fully-sampled data.

Original publication




Conference paper

Publication Date