Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiology, Stanford University, Stanford, CA 94305 USA.
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA.
Med Image Anal. 2021 Aug;72:102140. doi: 10.1016/j.media.2021.102140. Epub 2021 Jun 21.
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.
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，是由缺失、重复和未对准的图像数据引起的。本文提出了一种测地线密度回归（GDR）算法，通过使用其他呼吸相相应区域的无伪影数据来校正一个呼吸相中的伪影，从而纠正 4DCT 中的运动伪影。GDR 算法估计一个无伪影的肺模板图像和一个平滑、密集的 4D（空间加时间）向量场，该向量场将模板图像变形到每个呼吸相，生成一个无伪影的 4DCT 扫描。对应关系是通过考虑与空气进出肺部相关的局部组织密度变化，并使用二进制伪影掩模将包含伪影的区域从图像回归中排除来估计的。无伪影的肺模板图像是通过使用估计的对应关系将每个相体积的无伪影区域映射到公共参考坐标系，然后进行平均来生成的。该过程生成了具有改善的信噪比的肺部固定视图。使用模拟 CT 时间序列和具有临床观察到的运动伪影的 4DCT 扫描，对 GDR 算法进行了评估，并与最先进的测地线强度回归（GIR）算法进行了比较。该模拟表明，GDR 算法实现了更准确的雅可比图像和更清晰的模板图像，并且对数据丢失的敏感性低于 GIR 算法。我们还证明了 GDR 算法在去除治疗计划 4DCT 扫描中的临床观察到的运动伪影方面比 GIR 算法更有效。我们的代码可在 https://github.com/Wei-Shao-Reg/GDR 上免费获得。
