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摘要: 能谱CT可产生不同X射线能量下的基材料图像，所产生的基材料图像可用于组织成分和造影剂分布的定性与定量评价，且对成像物质分离、鉴别的能力明显优于传统单能CT。能谱CT中双能谱技术是最常用的模式之一，在临床应用中发挥了重大作用。本文就双能谱CT图像域基材料分解的两物质分解、多物质分解方法进行总结，最后展望未来可能的发展方向。Abstract: Spectral CT can produce basis materials with different X-ray energies. Subsequently, the generated basis materials can be used for qualitative and quantitative evaluation of tissue components and contrast agent distribution. This approach presents a superior ability to separate and identify imaging materials compared to traditional single-energy CT. Dual-energy spectrum technology is one of the most commonly used modes in spectrum CT, which plays an important role in clinical application. In this study, the decomposition methods of a basis material in the image domain of dual-energy spectrum CT were classified into two categories: two-material decomposition and multi-material decomposition. Finally, these methods are summarized and trend of future development is addressed.
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Keywords:
- spectral CT /
- dual-energy CT /
- image domain /
- basis material decomposition
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图 1 两种分解方法的模拟复现结果
（a）和（b）是直接矩阵求逆法分解的骨和软组织的基材料图像；（c）和（d）是迭代分解法分解的骨和软组织的基材料图像。
Figure 1. Simulation results obtained with the two decomposition methods
表 1 两种解决方案的主要研究
Table 1 Main research of two solutions
方法 文献 时间 优点 缺点 分解后去噪 [13] 1976 方法实现简单，计算效率高 由于图像分辨率损失很大，效果有限 [14] 1984 实现简便 效果有限 [15] 1985 实现简单 效果有限 [16] 1988 缓解了空间分辨率损失的问题 有边缘伪影 [17] 1995 算法可以在不考虑噪声相关性的情况下实现
噪声抑制分解后的图像中高频噪声被过度抑制，导致图像纹理的改变 [18] 2003 算法利用CT或分解图像的冗余结构或统计信
息进行噪声抑制，可以更好地抑制噪声没有完全描述DECT图像和分解图像之间的映射关系 分解前去噪 [19] 2014 使重建的两幅CT图像噪声变得强烈相关，进
而使得分解图像的噪声得到显著抑制CT重建和图像分解的结合增加了计算的复杂性，并且算法需要大量迭代才能收敛 [20] 2015 可以在保留定量测量和高频边缘信息的同
时显著降低噪声在心肌成像中仍会存在边缘效应 [21] 2018 在抑制噪声的同时可以保持图像边缘细节 没有考虑分解过程的噪声对图像的影响 [22] 2019 可获得高质量的重建CT图像以便后续分解 没有考虑分解过程的噪声对图像的影响 
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表 2 基于深度学习方法分解图像的主要研究
Table 2 Main research of image decomposition based on deep learning method
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