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.

翻译：可变形图像配准在医学成像中至关重要，有助于疾病诊断和图像引导干预。传统迭代方法速度较慢，而深度学习虽能加速求解，却面临可用性和精度方面的挑战。本研究引入了一种包含增强型运动分解Transformer（ModeTv2）算子的金字塔网络，展现了与传统方法类似的优越成对优化能力。我们通过CUDA扩展重新实现了ModeT算子以提升其计算效率，并进一步提出RegHead模块来细化形变场、增强形变真实性并减少参数。通过采用成对优化策略，所提网络在精度、效率和泛化能力之间取得了平衡。在两个公开脑部MRI数据集和一个腹部CT数据集上的大量实验证明，该网络适用于成对优化，提供了一种具有增强可用性和可解释性的深度学习模型。代码已公开。