摘要
CS理论是一种新兴的信号获取与处理理论,通过减少信号重建所需的数据(少于奈奎斯特定理所要求的最小数目),来缩短信号采样时间,减少计算量,并在一定程度上保持原有图像的重建质量。由于该理论的这些显著优点,使得其在医学成像领域引起了广泛关注,取得了很大进展。本文介绍了压缩感知理论在医学成像中的发展历程和最新进展,详细介绍一种基于字典学习的新型压缩感知自适应重建算法,最后通过计算机模拟实验对该方法进行了初步验证。
Compressed Sensing（CS） is a new signaln acquisition and processing theory.It can decrease the signal sampling time and computation cost by reducing the required data for signal recovery while maintaining good image quality.The CS theory has drawn a lot of attention and made great progress in medical imaging since it was proposed.This paper introduces the history of CS theory and the recent improvement in medical imaging. Moreover,we focus on the dictionary learning algorithm which is a new CS-based adaptive reconstruction algorithm.At last,the result of simulation is presented to convince the algorithm.
出处
《CT理论与应用研究（中英文）》
2012年第1期133-147,共15页
Computerized Tomography Theory and Applications
基金
国家自然科学基金(10905030
60871084)
北京市自然科学基金资助项目
关键词
CS理论
医学成像
图像重建
字典学习
K-SVD
compressed sensing
medical imaging
image construction
dictionary learning
K-SVD algorithm
