Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts.
Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts.
复制标题
用于校正4DCT肺呼吸运动伪影的地球密度回归。
DOI：
10.1016/j.media.2021.102140
复制
发表时间：
2021-08
影响因子：
10.9
通讯作者：
Christensen GE
中科院分区：
文献类型：
--
作者：
Shao W;Pan Y;Durumeric OC;Reinhardt JM;Bayouth JE;Rusu M;Christensen GE
肺部呼吸运动伪影在肺部的四维计算机断层扫描（4DCT）中很常见，并且是由丢失、重复和未对齐的图像数据引起的。本文提出了一种用于校正4DCT中运动伪影的测地线密度回归（GDR）算法，该算法通过使用来自其他呼吸相位的相应区域的无伪影数据来校正一个呼吸相位中的伪影。GDR算法估计无伪影的肺部模板图像和平滑、密集的4D（空间加时间）矢量场，该矢量场使模板图像变形到每个呼吸相位以产生无伪影的4DCT扫描。通过考虑与进入和离开肺部的空气相关联的局部组织密度变化，并使用二进制伪影掩模从图像回归中排除具有伪影的区域来估计对应性。通过使用估计的对应性将每个相位体积的无伪影区域映射到公共参考坐标系，然后求平均值，来生成无伪影肺模板图像。该过程生成具有改善的信噪比的肺的固定视图。使用模拟CT时间序列和4DCT扫描以及临床观察到的运动伪影，对GDR算法进行了评价，并与最先进的测地线强度回归（GIR）算法进行了比较。仿真结果表明，GDR算法获得了更精确的雅可比图像和更清晰的模板图像，并且比GIR算法对数据丢失更不敏感。我们还证明了GDR算法比GIR算法更有效地去除治疗计划4DCT扫描中临床观察到的运动伪影。我们的代码可在https://github.com/Wei-Shao-Reg/GDR上免费获取。建议的测地线密度回归（GDR）算法估计一个伪影免费模板图像平均掩蔽相位CT图像拉回到一个共同的坐标系。
Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR. The proposed geodesic density regression (GDR) algorithm estimates an artifact-free template image by averaging the masked phase CT images pulled back to a common coordinate system.
登录
查看更多内容
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
2.7
作者：
LIU, DC;NOCEDAL, J
通讯作者：
NOCEDAL, J
DOI：
10.1007/978-3-642-23629-7_80
发表时间：
2011
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Niethammer, Marc;Huang, Yang;Vialard, Francois-Xavier
通讯作者：
Vialard, Francois-Xavier
影响因子：
3.8
作者：
Amelon, Ryan E.;Cao, Kunlin;Reinhardt, Joseph M.;Christensen, Gary E.;Raghavan, Madhavan L.
通讯作者：
Raghavan, Madhavan L.
影响因子：
3.8
作者：
Ehrhardt, Jan;Werner, Rene;Handels, Heinz
通讯作者：
Handels, Heinz