基于稀疏约束TV最小化的CT图像重建
CT image reconstruction based on sparse constraint TV minimization
张旭 1董建 1张海宁 1杨耿煌1
作者信息
- 1. 天津职业技术师范大学自动化与电气工程学院,天津 300222;天津职业技术师范大学天津市信息传感与智能控制重点实验室,天津 300222
- 折叠
摘要
稀疏角度计算机断层扫描成像(computer tomography,CT)图像重建是一种降低CT扫描辐射剂量的重要方式.而经典的用于稀疏角度CT图像重建的广义迭代软阈值(generalization of the iterative soft-thresholding,GIST)算法收敛速度慢,不能满足临床对检查结果实时性的要求.全变分(total variation,TV)最小化算法在稀疏角度下既能减少条纹伪影又能较好地保留目标边界.文章提出稀疏约束的全变分算法,利用交替方向乘子法(alternate direction multiplier method,ADMM),将重建优化问题分解为2个子问题之和,有效提升了重建效果的同时提高了重建速度.实验结果表明:所提出的方法对数字模型图像和实际临床腹部图像都实现了高质量的图像重建,收敛速度较传统的同步迭代方法有大幅提升.
Abstract
Sparse angle computed tomography(CT)image reconstruction is an important way to reduce the radiation dose of CT scanning.However,the classic generalized iterative soft-thresholding(GIST)algorithm for sparse angle CT image reconstruction has slow convergence rates,and cannot meet the clinical requirement for real-time examination re-sults.The total variation(TV)minimization algorithm can reduce streak artifacts and well preserve the target boundary under sparse angles.The paper proposed a sparse constrained total variation algorithm,which utilized the alternating di-rection multiplier method(ADMM)to decompose the reconstruction optimization problem into the sum of two sub-problems,effectively improving the reconstruction effect and speed.Experimental results show that the proposed method achieves high-quality image reconstruction for both digital model images and actual clinical abdominal images,with a significantly improved convergence rate compared to traditional synchronous iterative methods.
关键词
稀疏角度/计算机断层扫描成像/全变分/交替方向乘子法Key words
sparse angle/computed tomography(CT)/total variation/alternating direction multiplier method(ADMM)引用本文复制引用
出版年
2024
NETL
NSTL
万方数据