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一个密集的U形变压器,具有双域多损失功能,用于稀疏视图CT重建

Peng Liu^1,2, Chenyun Fang¹, Zhiwei Qiao¹

¹School of Computer and Information Technology, Shanxi University, Taiyuan, China.

Journal of X-ray science and technology

|February 2, 2024

概括

一种新的深度学习方法,密集的U形变压器 (D-U-Transformer),有效地抑制了低剂量CT扫描中的文物. 这促进了高质量的成像,减少了辐射暴露.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

稀疏视图CT (SVCT) 对于降低辐射剂量至关重要,但在传统方法中存在缺陷.
在SVCT图像中的人工物降低了图像质量,并阻碍了准确的诊断.
开发先进的方法来抑制这些文物对于临床应用是必不可少的.

研究的目的:

开发一种基于深度学习的方法,用于抑制稀疏视图CT图像重建中的文物.
为了提高CT图像的质量,从稀疏视图投影重建.

主要方法:

提出了一个密集的U形变压器 (D-U-Transformer) 架构,结合了DenseNet和变压器.
在本地环境中利用密集连接的卷积,在远程依赖中利用变压器.
集成的道注意力和双域多损失功能用于优化.

主要成果:

与梅奥诊所LDCT数据集中的其他DL模型相比,D-U-Transformer在文物抑制和特征保存方面表现出卓越的性能.
废弃研究证实了D-U-变压器模型中的单个组件的有效性.
实现了高精度的SVCT重建.

结论:

拟议的D-U-转换器有效地抑制了CT成像中的稀疏工件.
这种方法促进了临床CT扫描中低剂量辐射和高质量的成像.
这些发现适用于CT和其他医学成像模式中的denoising和artefact移除.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.变压器变压器变压器深度卷积网络是一个深度卷积网络.多损失功能的多损失功能.稀疏视图重建的重建

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