论文标题
编码金属面膜投影，以减少计算机断层扫描
Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography
论文作者
论文摘要
计算机断层扫描（CT）中的金属伪影（MAR）是众所周知的挑战性任务，因为伪影在图像域中是结构性的，并且非本地。但是，它们本质上是正弦图领域中的局部性。因此，MAR的一种可能的方法是通过学习减少辛克图中的伪影来利用后者的特征。但是，如果我们直接将辛克图中受金属影响的区域视为缺失，并将其替换为神经网络产生的替代数据，则由于金属影响区域内的细粒细节完全忽略了伪影降低的CT图像往往会过度平滑和扭曲。在这项工作中，我们通过（1）保留在辛图中的金属影响区域来解决问题，并提议解决该问题，以及（2）用金属面膜投影替换二氧化金属痕迹，以便编码金属植入物的几何信息。对临床图像的模拟数据集和专家评估进行了广泛的实验表明，与最先进的方法相比，我们的新型网络在解剖学上会产生更精确的人工制品还原图像，尤其是当金属物体大的情况下。
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they are inherently local in the sinogram domain. Thus, one possible approach to MAR is to exploit the latter characteristic by learning to reduce artifacts in the sinogram. However, if we directly treat the metal-affected regions in sinogram as missing and replace them with the surrogate data generated by a neural network, the artifact-reduced CT images tend to be over-smoothed and distorted since fine-grained details within the metal-affected regions are completely ignored. In this work, we provide analytical investigation to the issue and propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection such that the geometry information of metal implants is encoded. Extensive experiments on simulated datasets and expert evaluations on clinical images demonstrate that our novel network yields anatomically more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.
