Respiration Correction by Clustering in Ultrasound Images

摘要

Respiratory motion is a challenging factor for image acquisition, image-guided procedures and perfusion quantification using contrast-enhanced ultrasound in the abdominal and thoracic region. In order to reduce the influence of respiratory motion, respiratory correction methods were investigated, hi this paper we propose a novel, cluster-based respiratory correction method. In the proposed method, we assign the image frames of the corresponding respiratory phase using spectral clustering firstly. And then, we achieve the images correction automatically by finding a cluster in which points are close to each other. Unlike the traditional gating method, we don't need to estimate the breathing cycle accurate. It is because images are similar at the corresponding respiratory phase, and they are close in high-dimensional space. The proposed method is tested on simulation image sequence and real ultrasound image sequence. The experimental results show the effectiveness of our proposed method in quantitative and qualitative.