Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under varying deformation requirements across images, leading to degraded performance. To solve these limitations, we propose iPEAR. Specifically, iPEAR adopts our proposed Fused Attention-Residual Module (FARM) for decoding, which comprises an attention pathway and a residual pathway to alleviate the accumulation of anatomical misalignment. We further propose a dual-stage Threshold-Controlled Iterative (TCI) strategy that adaptively determines the number of optimization iterations for varying images by evaluating registration stability and convergence. Extensive experiments on three public brain MRI datasets and one public abdomen CT dataset show that iPEAR outperforms state-of-the-art (SOTA) registration networks in terms of accuracy, while achieving on-par inference speed and model parameter size. Generalization and ablation studies further validate the effectiveness of the proposed FARM and TCI.

翻译：现有金字塔配准网络可能累积解剖结构错位，且缺乏有效机制在不同图像间动态确定优化迭代次数以适应变化的形变需求，导致性能下降。为解决这些局限性，我们提出iPEAR。具体而言，iPEAR采用我们提出的融合注意力-残差模块进行解码，该模块包含注意力路径和残差路径以缓解解剖错位的累积。我们进一步提出双阶段阈值控制迭代策略，通过评估配准稳定性和收敛性，自适应地确定不同图像所需的优化迭代次数。在三个公开脑部MRI数据集和一个公开腹部CT数据集上的大量实验表明，iPEAR在配准精度方面优于当前最先进的配准网络，同时达到相当的推理速度与模型参数量。泛化性与消融研究进一步验证了所提FARM与TCI策略的有效性。