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首页> 外文期刊>Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology >A Metal Artifact Reduction Method Using a Fully Convolutional Network in the Sinogram and Image Domains for Dental Computed Tomography
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A Metal Artifact Reduction Method Using a Fully Convolutional Network in the Sinogram and Image Domains for Dental Computed Tomography

机译：一种使用全卷积网络在牙科计算断层扫描中的中央图和图像域中的完全卷积网络的金属伪影减少方法

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摘要

The reconstruction quality of dental computed tomography (DCT) is vulnerable to metal implants because the presence of dense metallic objects causes beam hardening and streak artifacts in the reconstructed images. These metal artifacts degrade the images and decrease the clinical usefulness of DCT. Although interpolation-based metal artifact reduction (MAR) methods have been introduced, they may not be efficient in DCT because teeth as well as metallic objects have high X-ray attenuation. In this study, we investigated an effective MAR method based on a fully convolutional network (FCN) in both sinogram and image domains. The method consisted of three main steps: (1) segmentation of the metal trace, (2) FCN-based restoration in the sinogram domain, and (3) FCN-based restoration in image domain followed by metal insertion. We performed a computational simulation and an experiment to investigate the image quality and evaluated the effectiveness of the proposed method. The results of the proposed method were compared with those obtained by the normalized MAR method and the deep learning-based MAR algorithm in the sinogram domain with respect to the root-mean-square error and the structural similarity. Our results indicate that the proposed MAR method significantly reduced the presence of metal artifacts in DCT images and demonstrated better image performance than those of the other algorithms in reducing the streak artifacts without introducing any contrast anomaly.
机译：牙科计算断层扫描（DCT）的重建质量容易受金属植入物，因为致密金属物体的存在导致重建图像中的光束硬化和条纹伪像。这些金属伪影使图像降低并降低DCT的临床有用性。虽然已经引入了基于插值的金属伪像减少（MAR）方法，但由于齿和金属物体具有高X射线衰减，它们可能在DCT中不高。在这项研究中，我们研究了基于Scogram和图像域的完全卷积网络（FCN）的有效MAR方法。该方法包括三个主要步骤：（1）金属迹线的分割，（2）基于Scogram结构域的基于FCN的恢复，以及（3）在图像域中的基于FCN的恢复，然后是金属插入。我们执行了计算模拟和实验，以研究图像质量并评估所提出的方法的有效性。将所提出的方法的结果与由归一化MS法和基于深度学习的MAR算法获得的方法进行比较，相对于根均方误差和结构相似性。我们的结果表明，所提出的MAR方法显着降低了DCT图像中的金属伪影的存在，并比其他算法的图像表现出更好的图像性能，而不是在不引入任何对比异常的情况下进行条纹伪影。

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