Design and development of a nonrigid phantom for the quantitative evaluation of DIR-based mapping of simulated pulmonary ventilation
Design and development of a nonrigid phantom for the quantitative evaluation of DIR-based mapping of simulated pulmonary ventilation
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设计和开发非刚性体模，用于定量评估基于 DIR 的模拟肺通气映射
DOI：
10.1002/mp.13017
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发表时间：
2018
期刊：
影响因子：
3.8
通讯作者：
Sato Masanori
中科院分区：
文献类型：
--
作者：
Miyakawa Shin;Tachibana Hidenobu;Moriya Shunsuke;Kurosawa Tomoyuki;Nishio Teiji;Sato Masanori
目的基于可变形图像配准(DIR)的肺通气图的验证耗时长、易出错，且受变形参数的影响。在这项研究中，我们开发了一个非刚性体模作为一个质量保证(QA)工具，它模拟通风来定量评价基于DIR的图像。方法该体模由一个填充了用于模拟肺泡的聚亚安酯泡沫的丙烯酸圆柱体组成。在体模的下端附着一层聚氨基甲酸酯薄膜以模拟横隔膜。此外，将气管、支气管树状的聚亚安酯管插入泡沫塑料，并汇聚在模体外模拟气管。固体聚氨基甲酸酯也被用来模拟动脉，它紧跟着模型气道。在呼气期和吸气期分别进行两次三维(3D)CT扫描，分别使用氙气作为吸入造影剂。呼出的3D-CT图像被变形为吸入性3D-CT图像，使用我们基于NiftyReg开源包的内部程序。对于位于模拟肺体积中的16个标志点，计算了两幅图像之间的目标配准误差(TRE)。基于DIR的通风图像是使用雅可比行列式(JD)度量生成的。随后，测量两幅图像之间的Hounsfield单位(HU)值的差异。计算了JD和HU差值之间的相关系数。此外，还进行了3次4D-CT扫描，以评估体模运动和Xe气体分布的重复性。结果体模在每个地标上显示了不同的位移(范围为1-20 mm)。重现性分析表明，所有地标的位置差异均为<；1 mm，Xe气体分布的Hu变化接近于零。根据DIR软件进行空间精度评价的平均TRE为1.47±0.71 mm(最大为2.6 mm)。DIR软件的JD和HU差值之间有很大的相关性(R=−0.71)。结论该模型实现了新的特征，即变形和模拟通风。为了评估基于DIR的模拟肺通气图的准确性，该模型允许模拟Xe气体洗入和洗出。体模可能是一种有效的质量保证工具，因为DIR算法可以快速改变，并以高精度评估其准确性。
PurposeThe validation of deformable image registration (DIR)‐based pulmonary ventilation mapping is time consuming and prone to inaccuracies and is also affected by deformation parameters. In this study, we developed a nonrigid phantom as a quality assurance (QA) tool that simulates ventilation to evaluate DIR‐based images quantitatively.MethodsThe phantom consists of an acrylic cylinder filled with polyurethane foam designed to simulate pulmonic alveoli. A polyurethane membrane is attached to the inferior end of the phantom to simulate the diaphragm. In addition, tracheobronchial‐tree‐shaped polyurethane tubes are inserted through the foam and converge outside the phantom to simulate the trachea. Solid polyurethane is also used to model arteries, which closely follow the model airways. Two three‐dimensional (3D) CT scans were performed during exhalation and inhalation phases using xenon (Xe) gas as the inhaled contrast agent. The exhalation 3D‐CT image is deformed to an inhalation 3D‐CT image using our in‐house program based on the NiftyReg open‐source package. The target registration error (TRE) between the two images was calculated for 16 landmarks located in the simulated lung volume. The DIR‐based ventilation image was generated using Jacobian determinant (JD) metrics. Subsequently, differences in the Hounsfield unit (HU) values between the two images were measured. The correlation coefficient between the JD and HU differences was calculated. In addition, three 4D‐CT scans are performed to evaluate the reproducibility of the phantom motion and Xe gas distribution.ResultsThe phantom exhibited a variety of displacements for each landmark (range: 1–20 mm). The reproducibility analysis indicated that the location differences were <1 mm for all landmarks, and the HU variation in the Xe gas distribution was close to zero. The mean TRE in the evaluation of spatial accuracy according to the DIR software was 1.47 ± 0.71 mm (maximum: 2.6 mm). The relationship between the JD and HU differences had a large correlation (R= −0.71) for the DIR software.ConclusionThe phantom implemented new features, namely, deformation and simulated ventilation. To assess the accuracy of the DIR‐based mapping of the simulated pulmonary ventilation, the phantom allows for simulation of Xe gas wash‐in and wash‐out. The phantom may be an effective QA tool, because the DIR algorithm can be quickly changed and its accuracy evaluated with a high degree of precision.