论文标题
使用注意力引导的beta-cyclegan无监督的CT金属伪像学习
Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
论文作者
论文摘要
金属伪影（MAR）是计算机断层扫描（CT）中最重要的研究主题之一。随着图像重建的深度学习技术的发展，还建议了各种深度学习方法用于去除金属伪像，其中有监督的学习方法最受欢迎。但是，在实际CT采集中很难获得匹配的非金属和金属图像对。最近，使用特征分离构成了一个有希望的无监督学习，但由此产生的网络体系结构是并发症，难以处理大型临床图像。为了解决这个问题，我们在这里提出了一种更简单，有效的无监督的MAR方法。所提出的方法基于一种新型的β-循环结构，该结构从最佳传输理论中得出，用于适当的特征空间分离。另一个重要的贡献是表明注意机制是有效去除金属伪像的关键要素。具体而言，通过添加具有适当分离参数的卷积块注意模块（CBAM）层，实验结果证实我们可以得到更多改进的MAR，以保留原始图像的详细纹理。
Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning technology for image reconstruction,various deep learning methods have been also suggested for metal artifact removal, among which supervised learning methods are most popular. However, matched non-metal and metal image pairs are difficult to obtain in real CT acquisition. Recently, a promising unsupervised learning for MAR was proposed using feature disentanglement, but the resulting network architecture is complication and difficult to handle large size clinical images. To address this, here we propose a much simpler and much effective unsupervised MAR method for CT. The proposed method is based on a novel beta-cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Another important contribution is to show that attention mechanism is the key element to effectively remove the metal artifacts. Specifically, by adding the convolutional block attention module (CBAM) layers with a proper disentanglement parameter, experimental results confirm that we can get more improved MAR that preserves the detailed texture of the original image.
