残差混合注意力结合多分辨率约束的图像配准

Image registration based on residual mixed attention and multi-resolution constraints

医学图像配准在图谱创建和时间序列图像对比等临床应用中具有重要意义。目前,使用深度学习的配准方法与传统方法相比更好地满足了临床实时性的需求,但配准精确度仍有待提升。基于此,本文提出了一种结合残差混合注意力与多分辨率约束的配准模型MAMReg-Net,实现了脑部核磁共振成像(MagneticResonanceImaging,MRI)的单模态非刚性图像配准。该模型通过添加残差混合注意力模块,可以同时获取大量局部和非局部信息,在网络训练过程中提取到了更有效的大脑内部结构特征。其次,使用多分辨率损失函数来进行网络优化,实现更高效和更稳健的训练。在脑部T1 MR图像的12个解剖结构中,平均Dice分数达到0.817,平均ASD数值达到0.789,平均配准时间仅为0.34 s。实验结果表明,MAMReg-Net配准模型能够更好地学习脑部结构特征从而有效地提升配准精确度,并且满足临床实时性的需求。

Medical image registration has great significance in clinical applications such as atlas creation and time-series image comparison.Currently,in contrast to traditional methods,deep learning-based reg⁃istration achieves the requirements of clinical real-time;however,the accuracy of registration still needs to be improved.Based on this observation,this paper proposes a registration model named MAMReg-Net,which combines residual mixed attention and multi-resolution constraints to realize the non-rigid registration of brain magnetic resonance imaging(MRI).By adding the residual mixed attention module,the model can obtain a large amount of local and non-local information simultaneously,and extract more effec⁃tive internal structural features of the brain in the process of network training.Secondly,multi-resolution loss function is used to optimize the network to make the training more efficient and robust.The average dice score of the 12 anatomical structures in T1 brain MR images was 0.817,the average ASD score was 0.789,and the average registration time was 0.34 s.Experimental results demonstrate that the MAM⁃Reg-Net registration model can be better trained to learn the brain structure features to effectively improve the registration accuracy and meet clinical real-time requirements.