  Diffeomorphic image registration is a fundamental step in medical image
analysis, owing to its capability to ensure the invertibility of
transformations and preservation of topology. Currently, unsupervised
learning-based registration techniques primarily extract features at the image
level, potentially limiting their efficacy. This paper proposes a novel
unsupervised learning-based fully convolutional network (FCN) framework for
fast diffeomorphic image registration, emphasizing feature acquisition at the
image patch level. Furthermore, a novel differential operator is introduced and
integrated into the FCN architecture for parameter learning. Experiments are
conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI)
datasets. Comparative analyses with three state-of-the-art diffeomorphic image
registration approaches including a typical conventional registration algorithm
and two representative unsupervised learning-based methods, reveal that the
proposed method exhibits superior performance in both registration accuracy and
topology preservation.
