CT R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Suwon, Republic of Korea.
Sci Rep. 2021 Feb 12;11(1):3695. doi: 10.1038/s41598-021-83403-w.
A novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.
最近开发了一种用于 X 射线肺部 CT 成像的新型运动校正算法。它旨在为没有门控的常规胸部或胸部 CT 扫描（即轴向或螺旋扫描，螺距约为 1.0）执行。该算法利用两个共轭部分角度重建图像，通过非刚性配准进行运动估计，然后进行运动补偿重建。与其他传统方法不同，运动估计中不采用分割。这使得各种精细肺部结构的运动估计成为可能。本研究的目的是探索所提出的方法在纠正即使在常规带呼吸门控 CT 扫描下也会出现的肺部运动伪影的性能。众所周知，这些伪影模拟各种肺部疾病，因此解决这个问题非常重要。为此，进行了移动体模实验和临床研究（七例）。我们选择熵和正性作为评价指标，比较运动校正前后的重建图像。体模和临床研究的结果均表明，所提出的方法具有统计学意义的改善，即通过正性测量分别提高了 53.6%（p<0.05）和 35.5%（p<0.05）。所提出的方法的图像显示，肺部实质、肺血管和气道等各种肺部结构的运动伪影明显减少，而 FBP 图像则明显减少。两个示例病例的结果也表明，所提出的方法在纠正主动脉运动伪影方面具有很大的潜力，主动脉运动伪影已知会模拟主动脉夹层。与其他方法相比，所提出的方法提供了出色的性能和全自动工作流程。此外，它在处理肺部结构和主动脉等广泛器官的运动方面具有很大的潜力。我们期望这将为胸部和胸部 CT 成像的创新铺平道路。
