Pan Jiayi, Zhang Heye, Wu Weifei, Gao Zhifan, Wu Weiwen
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China.
Department of Orthopedics, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, Hubei, China.
Patterns (N Y). 2022 Apr 22;3(6):100498. doi: 10.1016/j.patter.2022.100498. eCollection 2022 Jun 10.
Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors.
将投影视图减少到较低的X射线辐射剂量通常会导致严重的条纹伪影。为了从稀疏视图数据中提高图像质量，本文开发并报道了一种多域集成Swin变压器网络（MIST-net）。首先，MIST-net使用灵活的网络架构合并了来自数据、残差数据、图像和残差图像的丰富域特征，其中残差数据和残差图像子网络被视为数据一致性模块，以消除插值和重建误差。其次，引入了一个可训练的边缘增强滤波器来检测和保护图像边缘。第三，设计了一个高质量重建Swin变压器（即Recformer）来捕捉图像全局特征。在具有48个视图的数值和真实心脏临床数据集上的实验结果表明，我们提出的MIST-net比其他竞争对手提供了更好的图像质量，具有更多的小特征和清晰的边缘。
