摘要
成像速度慢制约了核磁共振在临床医学诊断中的应用,压缩感知理论提供了解决问题的新思路,可以利用图像稀疏性压缩信息并通过数学模型重建图像,方法的关键是模型和算法。本文提出了联合先验的重建模型,改进了迭代阈值法和增广拉格朗日法,并通过对比实验证明了优化后图像重建方法的有效性。
The slow imaging speed restricts the application of nuclear magnetic resonance, and compressive sensing theory provides a new way to solve the problem, which can compress information Based on image sparsity and reconstruct image via mathematical model.This paper proposes a joint priori reconstruction model, improves the iterative threshold method and the Lagrange method, and proves the effectiveness of image reconstruction method by comparison experiments.
作者
郭姿鹬
GUO Zi-yu(College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《电脑知识与技术》
2020年第4期211-212,300,共3页
Computer Knowledge and Technology
关键词
核磁共振图像
压缩感知
联合先验模型
拉格朗日算法
nuclear magnetic resonance
compressive sensing
joint priori model
Lagrange algorithm
