We introduce an end-to-end deep-learning framework for 3D medical image
registration. In contrast to existing approaches, our framework combines two
registration methods: an affine registration and a vector
momentum-parameterized stationary velocity field (vSVF) model. Specifically, it
consists of three stages. In the first stage, a multi-step affine network
predicts affine transform parameters. In the second stage, we use a Unet-like
network to generate a momentum, from which a velocity field can be computed via
smoothing. Finally, in the third stage, we employ a self-iterable map-based
vSVF component to provide a non-parametric refinement based on the current
estimate of the transformation map. Once the model is trained, a registration
is completed in one forward pass. To evaluate the performance, we conducted
longitudinal and cross-subject experiments on 3D magnetic resonance images
(MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show
that our framework achieves comparable performance to state-of-the-art medical
image registration approaches, but it is much faster, with a better control of
transformation regularity including the ability to produce approximately
symmetric transformations, and combining affine and non-parametric
registration.

本文介绍了一种基于深度学习框架的三维医学图像配准方法，结合仿射配准和向量动量参数固定速度场（vSVF）模型，具有速度快、精度高、可对称变换等特点，在磁共振成像方面表现出了优异的性能。