  We propose a fluid-based registration framework of medical images based on
implicit neural representation. By integrating implicit neural representation
and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a
Multilayer Perceptron (MLP) as a velocity generator while optimizing velocity
and image similarity. Moreover, we adopt a coarse-to-fine approach to address
the challenge of deformable-based registration methods dropping into local
optimal solutions, thus aiding the management of significant deformations in
medical image registration. Our algorithm has been validated on a paired
CT-CBCT dataset of 50 patients,taking the Dice coefficient of transferred
annotations as an evaluation metric. Compared to existing methods, our approach
achieves the state-of-the-art performance.
