Multiple measurements vectors compressed sensing for Doppler ultrasound signal reconstruction

Abstract

Compressed sensing (CS) is a novel framework for reconstruction images and signals. In this work we want to make use of the latest sampling theory multiple measurement vectors (MMV) compressed sensing model, to reconstruct the Doppler ultrasound signal. Compressed sensing theory states that it is possible to reconstruct images or signals from fewer numbers of measurements. In usual CS algorithms, the measurement matrix is vectors so the single measurement vectors (SMV) applied to generate a sparse solution. Instead of using the SMV model we want to make use of the MMV model to generate the sparse solution in this work. Doppler ultrasound is one of the most important imaging techniques. To acquire the images much data were needed, which cause increased in process time and other problems such as increasing heating per unit and increasing the amount of the data that needed for reconstruction. To overcome these problems we proposed data acquisition based on compressed sensing framework. The result shows that the Doppler signal can be reconstructed perfectly by using compressed sensing framework.