Department of Bioengineering, TBM Laboratory Politecnico di Milano, Milano 20133, Italy.
Med Phys. 2011 Feb;38(2):656-67. doi: 10.1118/1.3538921.
Artifacts affect 4D CT images due to breathing irregularities or incorrect breathing phase identification. The purpose of this study is the reduction of artifacts in sorted 4D CT images. The assumption is that the use of multiple respiratory related signals may reduce uncertainties and increase robustness in breathing phase identification.
Multiple respiratory related signals were provided by infrared 3D localization of a configuration of markers placed on the thoracoabdominal surface. Multidimensional K-means clustering was used for retrospective 4D CT image sorting, which was based on multiple marker variables, in order to identify clusters representing different breathing phases. The proposed technique was tested on computational simulations, phantom experimental acquisitions, and clinical data coming from two patients. Computational simulations provided a controlled and noise-free condition for testing the clustering technique on regular and irregular breathing signals, including baseline drift, time variant amplitude, time variant frequency, and end-expiration plateau. Specific attention was given to cluster initialization. Phantom experiments involved two moving phantoms fitted with multiple markers. Phantoms underwent 4D CT acquisition while performing controlled rigid motion patterns and featuring end-expiration plateau. Breathing cycle period and plateau duration were controlled by means of weights leaned upon the phantom during repeated 4D CT scans. The implemented sorting technique was applied to clinical 4D CT scans acquired on two patients and results were compared to conventional sorting methods.
For computational simulations and phantom studies, the performance of the multidimensional clustering technique was evaluated by measuring the repeatability in identifying the breathing phase among adjacent couch positions and the uniformity in sampling the breathing cycle. When breathing irregularities were present, the clustering technique consistently improved breathing phase identification with respect to conventional sorting methods based on monodimensional signals. In patient studies, a qualitative comparison was performed between corresponding breathing phases of 4D CT images obtained by conventional sorting methods and by the described clustering technique. Artifact reduction was clearly observable on both data set especially in the lower lung region.
The implemented multiple point method demonstrated the ability to reduce artifacts in 4D CT imaging. Further optimization and development are needed to make the most of the availability of multiple respiratory related variables and to extend the method to 4D CT-PET hybrid scan.
由于呼吸不规则或呼吸相位识别不正确，伪影会影响 4D CT 图像。本研究的目的是减少分类 4D CT 图像中的伪影。假设使用多个与呼吸相关的信号可能会降低呼吸相位识别中的不确定性并提高稳健性。
通过对放置在胸腹部表面的标记物配置进行的红外 3D 定位，提供了多个与呼吸相关的信号。多维 K-均值聚类用于基于多个标记变量的回顾性 4D CT 图像分类，以识别代表不同呼吸阶段的聚类。所提出的技术在计算模拟、体模实验采集和来自两名患者的临床数据上进行了测试。计算模拟为在规则和不规则呼吸信号上测试聚类技术提供了一个受控且无噪声的条件，包括基线漂移、时变幅度、时变频率和呼气末平台。特别关注了聚类初始化。体模实验涉及两个配备多个标记物的移动体模。体模在执行受控刚性运动模式并具有呼气末平台时进行 4D CT 采集。通过在重复 4D CT 扫描期间倚靠在体模上的权重来控制呼吸周期周期和平台持续时间。将实施的分类技术应用于两名患者的临床 4D CT 扫描，并将结果与常规分类方法进行比较。
对于计算模拟和体模研究，通过测量在相邻治疗床位置之间识别呼吸相位的重复性和在呼吸周期中采样的均匀性来评估多维聚类技术的性能。当存在呼吸不规则时，与基于单维信号的常规分类方法相比，聚类技术始终能够改善呼吸相位识别。在患者研究中，对常规分类方法和所描述的聚类技术获得的 4D CT 图像的相应呼吸相位进行了定性比较。在两个数据集上都可以明显观察到伪影减少，尤其是在肺部较低区域。
所实现的多点方法证明了减少 4D CT 成像中伪影的能力。需要进一步优化和开发，以充分利用多个与呼吸相关的变量，并将该方法扩展到 4D CT-PET 混合扫描。
