非刚性循环一致的双向网络与无监督可变形功能磁共振成像注册的变压器
Yingying Wang1, Yu Feng1, Weiming Zeng1
1Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Brain sciences
|January 24, 2025
概括
这项研究引入了一种功能性MRI (fMRI) 的新型非刚性注册方法,可以提高受试者之间的结构和功能一致性. 这种新方法通过更好地调整大脑图像来改善神经科学研究中的统计分析.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 医学图像分析 医学图像分析
背景情况:
- 准确的跨主体注册对于功能磁共振成像 (fMRI) 研究中的统计分析至关重要.
- 使用结构性MRI的传统方法与功能一致性作斗争,因为解剖学和功能区域可能不一致.
- 新兴的基于功能的方法往往忽略了有价值的结构信息.
研究的目的:
- 为fMRI开发一种无监督,非严格的注册方法,将结构和功能信息整合在一起.
- 提高跨主体fMRI注册的准确性和可靠性,以进行增强的统计分析.
- 在fMRI数据中实现更高的结构和功能一致性.
主要方法:
- 提出了一个非刚性,循环一致,双向网络与变压器架构用于无监督可变形fMRI注册.
- 通过提取本地功能连接模式和特征来集成功能信息.
- 采用双向网络进行前向和反向注册,并使用变压器进行远程空间映射.
主要成果:
- 与传统的 (Affine,Syn) 和基于学习的 (Transmorph-tiny,Cyclemorph,Voxelmorph x2) 方法相比,提出的方法证明了更好的注册性能.
- 在注册后,在关键大脑网络 (DMN,VN,CEN,SMN) 中实现了更高的峰值t值和更多的超值voxels.
- 在多个网络中显示了峰值t值的显著平均改善,这表明功能对齐得到了增强.
结论:
- 开发的fMRI注册方法有效地提高了受试者之间的结构和功能一致性.
- 基于变压器的网络促进了结构和功能信息的整合,从而实现了卓越的注册性能.
- 这一进步有望在使用fMRI数据的神经科学研究中进行更强大的统计分析.
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