Image registration is essential for medical image applications where
alignment of voxels across multiple images is needed for qualitative or
quantitative analysis. With recent advancements in deep neural networks and
parallel computing, deep learning-based medical image registration methods
become competitive with their flexible modelling and fast inference
capabilities. However, compared to traditional optimization-based registration
methods, the speed advantage may come at the cost of registration performance
at inference time. Besides, deep neural networks ideally demand large training
datasets while optimization-based methods are training-free. To improve
registration accuracy and data efficiency, we propose a novel image
registration method, termed Recurrent Inference Image Registration (RIIR)
network. RIIR is formulated as a meta-learning solver to the registration
problem in an iterative manner. RIIR addresses the accuracy and data efficiency
issues, by learning the update rule of optimization, with implicit
regularization combined with explicit gradient input.
We evaluated RIIR extensively on brain MRI and quantitative cardiac MRI
datasets, in terms of both registration accuracy and training data efficiency.
Our experiments showed that RIIR outperformed a range of deep learning-based
methods, even with only $5\%$ of the training data, demonstrating high data
efficiency. Key findings from our ablation studies highlighted the important
added value of the hidden states introduced in the recurrent inference
framework for meta-learning. Our proposed RIIR offers a highly data-efficient
framework for deep learning-based medical image registration.

通过提出一种名为 RIIR 的新型图像注册方法，该方法将深度学习技术应用于医学图像注册中，提高了注册准确性和数据利用效率。