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基于量子粒子群算法的CT系统参数标定 被引量:1

Parameter Calibration of CT System Based on Quantum Particle Swarm Optimization
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摘要 量子计算是一种遵循量子力学规律调控量子信息单元进行计算的新型计算模式,在计算效率上,量子算法在处理问题时速度要快于传统的通用计算机。提出了一种基于量子粒子群算法的CT系统参数标定。该方法首先根据螺旋CT原理和几何关系,对参数进行大致的求解,随后利用量子粒子群算法对参数进行优化,利用高斯滤波对图像进行优化,以此实现较高精度的CT系统参数标定,实验结果表明该方法不仅可以实现高效率的CT成像,而且对量子计算理论的实际应用的推广有重大意义。 Quantum computing is a new computing mode that follows the laws of quantum mechanics to regulate the quantum information unit for calculation. In terms of computational efficiency, the quantum algorithm is faster in reprocessing problems than the traditional general purpose computer. A calibration method of CT system parameters based on quantum particle swarm optimization (QPSO) is proposed. In this algorithm, firstly according to the principle of spiral CT and geometric relations, approximate solving the parameters, and then use a quantum particle swarm optimization (QPSO) algorithm to optimize the parameters, and optimize the image, using Gaussian filter to realize high accuracy of CT system parameter calibration, the experimental results show that this method not only can realize high efficiency of CT imaging, but also for the promotion of practical application of quantum computation theory has great significance.
出处 《应用数学进展》 2021年第5期1607-1615,共9页 Advances in Applied Mathematics
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