基于二维压缩感知的超声k空间重建

K-space Reconstruction for Ultrasound Imaging by Two Dimensional Compressed Sensing

压缩感知理论已经应用于多个领域,如MRI,超声等。提出了一个新的超声重建方法,基于二维压缩感知和k空间。在时域采样特定信号,并在k空间中对其进行重建。当处理二维信号(图像)时,传统的方法是将其转换为一维向量,这种方法非常耗时,并且对于大的图像重建来说不切实际,通过二维压缩感知处理这个难题。

Compressed sensing or Compressive sampling （CS）theory has been adapted to several instrumentation requirements, such as MRI,Ultrasound.This paper proposes a novel ultrasound image reconstruction method based on two dimension compressed sensing （2D CS） and k-space reconstruction.This paper samples the image of interest in the spatial domain, and reconstruct it in the k-space domain.To deal with 2D signals （images）,the conventional treatment is to convert them into one dimension （1D） vectors,which is time-consuming and impractical for large image reconstruction,use here the 2D CS instead.Several comparisons between proposed algorithm and conventional algorithms are given in this paper.