Robins Marthony, Solomon Justin, Sahbaee Pooyan, Sedlmair Martin, Roy Choudhury Kingshuk, Pezeshk Aria, Sahiner Berkman, Samei Ehsan
Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States of America.
Phys Med Biol. 2017 Aug 22;62(18):7280-7299. doi: 10.1088/1361-6560/aa83f8.
Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDI). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule's location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation ([Formula: see text], [Formula: see text] and [Formula: see text] of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the ([Formula: see text]) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.
虚拟结节插入为开发具有已知病变的混合CT图像标准化数据库铺平了道路。本研究的目的是评估三种将虚拟肺结节插入CT图像的方法（一种既定方法和两种新开发的技术）。通过将虚拟结节的体积和形状与合成结节的CT衍生体积和形状进行比较来进行评估。将24个合成结节（三种大小、四种形态、两个重复）物理插入到拟人化胸部模型（京都科学株式会社）的肺腔中。使用标准胸部CT协议在两个剂量水平（1.4和22 mGy CTDI）下，在商用CT扫描仪（西门子SOMATOM Definition Flash）上对有结节和无结节的模型进行成像。保存原始投影数据，并使用滤波反投影和正弦图确认迭代重建（SAFIRE，强度5）以0.6毫米的切片厚度进行重建。将相应的3D理想化虚拟结节模型与CT图像配准，以确定每个结节的位置和方向。使用两种方法将虚拟结节体素化、进行部分体积校正并插入无结节的CT数据中（考虑系统成像物理）：基于投影的技术A和基于图像的技术B。还测试了第三种技术C，即从真实结节的采集图像中裁剪感兴趣区域并将其融合到无结节图像中。使用商用分割工具（iNtuition，TeraRecon公司）测量结节体积，并使用豪斯多夫距离评估变形。使用线性混合效应回归模型比较理想化、CT衍生和虚拟结节之间的结节体积和变形，该模型利用区域豪斯多夫距离的均值、标准差和变异系数（[公式：见正文]、[公式：见正文]和[公式：见正文]）。总体而言，CT衍生结节和虚拟结节的体积之间有密切的一致性。对于所有插入技术，它们之间的百分比差异小于3%，并且在大多数情况下无统计学意义。相关系数值大于0.97。对于技术A、B和C，CT衍生结节和虚拟结节之间根据豪斯多夫距离的变形也相似，在（[公式：见正文]）中具有最小的统计学意义。本研究表明，基于投影和基于图像的结节插入技术都能生成与合成结节在结节体积和变形方面具有统计相似性的逼真结节渲染图。这些技术可用于创建包含已知大小、位置和形态结节的混合CT图像数据库。
