压缩感知在医学图像重建中的最新进展

基金项目:

北京市自然科学基金资助项目

国家自然科学基金(10905030,60871084)

详细信息

New Advances of Compressed Sensing in Medical Image Reconstruction

1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China

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。
- CS理论 /
- 医学成像 /
- 图像重建 /
- 字典学习 /
- K-SVD
Abstract: 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.
- compressed sensing /
- medical imaging /
- image construction /
- dictionary learning /
- K-SVD algorithm

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1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China