Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the target area to critical anatomical structures during surgery. However, due to the differences between multimodal images and intraoperative image deformation caused by tissue displacement and removal during the surgery, effective registration of preoperative and intraoperative multimodal images faces significant challenges. To address the multimodal image registration challenges in Learn2Reg 2025, an unsupervised multimodal medical image registration method based on Multilevel Correlation Pyramidal Optimization (MCPO) is designed to solve these problems. First, the features of each modality are extracted based on the modality independent neighborhood descriptor, and the multimodal images is mapped to the feature space. Second, a multilevel pyramidal fusion optimization mechanism is designed to achieve global optimization and local detail complementation of the displacement field through dense correlation analysis and weight-balanced coupled convex optimization for input features at different scales. Our method focuses on the ReMIND2Reg task in Learn2Reg 2025. Based on the results, our method achieved the first place in the validation phase and test phase of ReMIND2Reg. The MCPO is also validated on the Resect dataset, achieving an average TRE of 1.798 mm. This demonstrates the broad applicability of our method in preoperative-to-intraoperative image registration. The code is available at https://github.com/wjiazheng/MCPO.

翻译：基于多模态图像配准的手术导航通过术中显示目标区域与关键解剖结构的相对位置，为外科医生提供了重要的术中引导。然而，由于多模态图像间的差异以及术中组织位移和切除导致的图像形变，术前与术中多模态图像的有效配准面临重大挑战。为应对Learn2Reg 2025中的多模态图像配准挑战，本文设计了一种基于多级相关金字塔优化的无监督多模态医学图像配准方法。首先，基于模态无关邻域描述符提取各模态特征，将多模态图像映射至特征空间。其次，设计了多级金字塔融合优化机制，通过对不同尺度输入特征进行密集相关性分析和权重平衡耦合凸优化，实现位移场的全局优化与局部细节互补。本方法聚焦于Learn2Reg 2025中的ReMIND2Reg任务。实验结果表明，本方法在ReMIND2Reg的验证阶段和测试阶段均取得第一名。MCPO方法同时在Resect数据集上得到验证，实现了1.798毫米的平均靶向配准误差。这证明了本方法在术前-术中图像配准中具有广泛适用性。代码已开源：https://github.com/wjiazheng/MCPO。