Deformable image registration plays a crucial role in medical imaging, aiding
in disease diagnosis and image-guided interventions. Traditional iterative
methods are slow, while deep learning (DL) accelerates solutions but faces
usability and precision challenges. This study introduces a pyramid network
with the enhanced motion decomposition Transformer (ModeTv2) operator,
showcasing superior pairwise optimization (PO) akin to traditional methods. We
re-implement ModeT operator with CUDA extensions to enhance its computational
efficiency. We further propose RegHead module which refines deformation fields,
improves the realism of deformation and reduces parameters. By adopting the PO,
the proposed network balances accuracy, efficiency, and generalizability.
Extensive experiments on two public brain MRI datasets and one abdominal CT
dataset demonstrate the network's suitability for PO, providing a DL model with
enhanced usability and interpretability. The code is publicly available.

本研究介绍了一种采用金字塔网络和增强型运动分解 Transformer (ModeTv2) 操作符的可变形图像配准方法，展示了与传统方法相似的优越的成对优化（PO）能力。通过使用 CUDA 扩展重新实现 ModeT 操作符以提高计算效率，并提出了 RegHead 模块以改进变形场的真实性和减少参数。采用成对优化（PO），所提出的网络在两个公开的脑 MRI 数据集和一个腹部 CT 数据集上进行了广泛实验证明其适用于 PO，提供了一种具有增强的易用性和可解释性的深度学习模型。代码公开可用。