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.
@article{arxiv.2510.07666,
title = {iPEAR: Iterative Pyramid Estimation with Attention and Residuals for Deformable Medical Image Registration},
author = {Heming Wu and Di Wang and Tai Ma and Peng Zhao and Yubin Xiao and Zhongke Wu and Xing-Ce Wang and Xuan Wu and You Zhou},
journal= {arXiv preprint arXiv:2510.07666},
year = {2026}
}