Learning metal artifact reduction in cardiac CT images with moving pacemakers
Learning metal artifact reduction in cardiac CT images with moving pacemakers
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DOI：
10.1016/j.media.2020.101655
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发表时间：
2020-04-01
影响因子：
10.9
通讯作者：
Grass, M.
中科院分区：
文献类型：
--
作者：
Lossau (Nee Elss), T.;Nickisch, H.;Grass, M.
人体心脏中的金属物体，如植入式起搏器，在重建的CT图像中经常会产生沉重的伪影。由于心脏运动的原因，在CT采集过程中假定静止物体的常见金属伪影减小方法不适用。本文提出了一种由三个卷积神经网络（CNN）集成构成的全自动动态起搏器伪影还原（DyPAR+）管道。第一步，使用segmentnets将起搏器金属阴影直接分割到原始投影数据中。其次，生成的金属阴影掩模被传递到InpaintingNets中，InpaintingNets替换sinogram中受金属影响的线积分，用于随后的无金属图像体重建。第三，在预先选择的运动状态下的金属位置由ReinsertionNets基于由分割的金属阴影蒙版生成的部分角度反向投影堆栈来预测。我们通过将合成的、可移动的起搏器引线引入14个没有起搏器的临床病例，生成了监督学习过程所需的数据。在使用合成金属引线对临床数据进行测试时，segmentnets和ReinsertionNets的平均Dice系数分别为94.16% +/- 2.01%和55.60% +/- 4.79%。InpaintingNets在图像域的平均绝对重建误差为11.54 HU +/- 2.49 HU，优于手工制作的PatchMatch和逆距离加权方法。将提议的DyPAR+管道应用于9个真实起搏器的临床测试案例，显著减少了金属伪影，并证明了临床实践的可移植性。尤其是SegmentationNets和InpaintingNets，可以很好地推广到不可见的获取模式和对比协议。(C) 2020 Elsevier B.V.版权所有
Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are not applicable.We propose a fully automatic Dynamic Pacemaker Artifact Reduction (DyPAR+) pipeline which is built of three convolutional neural network (CNN) ensembles. In a first step, pacemaker metal shadows are segmented directly in the raw projection data by the SegmentationNets. Second, resulting metal shadow masks are passed to the InpaintingNets which replace metal-affected line integrals in the sinogram for subsequent reconstruction of a metal-free image volume. Third, the metal locations in a pre-selected motion state are predicted by the ReinsertionNets based on a stack of partial angle back-projections generated from the segmented metal shadow mask. We generate the data required for the supervised learning processes by introducing synthetic, moving pacemaker leads into 14 clinical cases without pacemakers.The SegmentationNets and the ReinsertionNets achieve average Dice coefficients of 94.16% +/- 2.01% and 55.60% +/- 4.79% during testing on clinical data with synthetic metal leads. With a mean absolute reconstruction error of 11.54 HU +/- 2.49 HU in the image domain, the InpaintingNets outperform the hand-crafted approaches PatchMatch and inverse distance weighting. Application of the proposed DyPAR+ pipeline to nine clinical test cases with real pacemakers leads to significant reduction of metal artifacts and demonstrates the transferability to clinical practice. Especially the SegmentationNets and InpaintingNets generalize well to unseen acquisition modes and contrast protocols. (C) 2020 Elsevier B.V. All rights reserved.