基于低等级和基于相似性的规范化算法的投影域分解
Chang Lu1, Zhenye Han1, Jing Zou1
1The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.
Journal of X-ray science and technology
|April 19, 2024
概括
一个新的低级和基于相似性的规范化 (LRSBR) 算法有效地减少了双能CT成像中的噪声. 这种方法保留了图像细节,提高了投影域分解 (PDD) 应用程序的准确性.
科学领域:
- 医疗成像医学成像
- 图像重建 图像的重建
- 计算成像技术的成像
背景情况:
- 投影域分解 (PDD) 是一种双能重建方法.
- PDD有效地减少了光束硬化和金属工件,但受到噪声放大的影响.
- 降低噪声对于在PDD中准确有效的原子数和电子密度估计至关重要.
研究的目的:
- 开发一种新的算法,用于在双能CT中最小化噪声.
- 在降噪过程中保留图像边缘和细节.
- 提高基于PDD的重建的准确性和视觉质量.
主要方法:
- 引入了一种基于低级别和基于相似性的规范化 (LRSBR) 的排名算法.
- 将张量低级属性集成到基于相似性的规范化 (SBR) 框架中.
- 解决了SBR边缘像素的不稳定性,改善了结构一致性.
主要成果:
- 与现有方法相比,LRSBR算法在PSNR,RMSE和SSIM中表现出优异的性能.
- 对双层双能CT系统的实验证实了显著的降噪.
- CT图像的视觉质量显示出显著的增强.
结论:
- 拟议的LRSBR算法为双能CT提供了显著改进的降噪.
- 在保护边缘和精细结构方面,LRSBR卓越.
- 该方法对于投射域分解应用来说是实用的和有益的.
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