Inverse problem of ultrasound beamforming with sparsity in time and frequency domain

Abstract

Beamforming is a common signal processing technique to locally focus and enhance the transmitted or received ultrasound (US) energy in a region-of-interest (ROI), which has an important impact on the image quality and controls the resolution and contrast. The most common and the simplest beamforming technique for medical US is the Delay-And-Sum (DAS) beamforming, although adaptive beamforming techniques also exist. Row-based beamforming methods formulate the forward problem for each image row and accordingly solve an inverse problem separately at each image depth. In this work, we propose a full-frame inverse-problem based beamforming method, where we define the forward problem for the entire image and solve an inverse problem for all image depths jointly. We present our results for the reconstruction of the US image using one plane-wave. Results are evaluated on the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS) dataset. CNR and FWHM values for all datasets are shown to improve significantly in comparison to DAS.