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BCSwinReg:CBCT到CT多式模式图像注册的跨模式关注网络

Jieming Zhang¹, Chang Qing¹, Yu Li¹

¹The East China University of Science and Technology, Shanghai, 200237, China.

Computers in biology and medicine

|February 20, 2024

概括

这项研究介绍了BCSwinReg和CrossSwin,用于改善放射治疗中的计算机断层扫描 (CT) 和束计算机断层扫描 (CBCT) 图像记录. 这些方法提高了多式联运注册的准确性,这对于精确的辐射传递至关重要.

科学领域:

医疗成像医学成像
放射治疗物理物理
计算机视觉计算机视觉

背景情况:

计算机断层扫描 (CT) 和圆束计算机断层扫描 (CBCT) 的准确记录对于放射治疗的规划和实施至关重要.
固有的CBCT图像的低质量为强大的多式联络图像注册带来了重大挑战.

研究的目的:

开发一个有效的框架来挑战CBCT-CT多式模式图像注册.
增强特征融合和域映射,以改善医学成像模式之间的对应学习.

主要方法:

提出了一个新的骨干网络,BCSwinReg,包含一个交叉模式的注意模块 (CrossSwin).
交叉旋转方便多模式功能融合和域映射到一个共同的空间.
BCSwinnReg使用交叉注意力来交换信息,用于语义信息的多分辨率策略,以及用于变形集成的级联方法.

主要成果:

在4D-Lung数据集上的实验证明了CrossSwin和BCSwinReg的有效性.
与VoxelMorph相比,BCSwinnReg在子相似系数 (DSC) 中取得了3.3%的改善.
在平均95%的豪斯多夫距离 (HD95) 中,BCSwinnReg比Voxelmorph有0.19的改善.

结论:

拟议的CrossSwin模块和BCSwinReg网络显著提高了CBCT-CT多式联运注册的准确性.
这些进步通过克服CBCT图像质量限制,为放射治疗应用提供了更高的精度.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.圆束计算机断层扫描技术跨领域的注意力网络.深度学习是一种深度学习.医疗图像注册医疗图像注册

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