Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, United States of America.
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, United States of America.
Phys Med Biol. 2024 Apr 30;69(10):105012. doi: 10.1088/1361-6560/ad3db9.
Cardiac computed tomography (CT) is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The goal of this paper is to reconstruct high quality cardiac CT images from limited-angle projections.. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution.. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods.. It has a great potential to freeze the beating heart with a higher temporal resolution.
心脏计算机断层扫描（CT）广泛用于诊断心血管疾病，这是世界上发病率和死亡率的主要原因。诊断性能强烈依赖于 CT 图像的时间分辨率。为了对跳动的心脏进行成像，可以通过获取有限角度的投影来减少扫描时间。然而，这会导致图像噪声增加和与有限角度相关的伪影。本文的目的是从有限角度的投影重建高质量的心脏 CT 图像。从有限角度的投影重建高质量图像的能力是非常需要的，仍然是一个主要的挑战。随着深度学习网络的发展，如 U-Net 和 transformer 网络，在图像重建和处理方面取得了进展。在这里，我们提出了一种基于 U-Net 和 Swin-transformer（U-Swin）网络的混合模型。U-Net 有可能恢复由于缺少投影数据和相关伪影而丢失的结构信息，然后 Swin-transformer 可以收集详细的全局特征分布。使用合成的 XCAT 和临床心脏 COCA 数据集，我们证明了我们提出的方法优于最先进的基于深度学习的方法。它有很大的潜力以更高的时间分辨率冻结跳动的心脏。
