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<p>Deformable image registration is a critical task in medical image analysis. However, existing learning-based methods encounter difficulties in achieving high registration accuracy under large anatomical deformations while maintaining model interpretability. To overcome these limitations, a correlation reconstruction and refinement network (CRR-Net) was introduced, representing the first framework to incorporate super-resolution reconstruction at the feature level for deformable registration. The core innovation lies in the correlation reconstruction and refinement module (CRRM), which enables precise modeling of spatial correspondences by leveraging high-resolution feature spaces. This design captures richer structural details and contextual cues while expanding the receptive field during resolution recovery, thereby improving performance in large-deformation scenarios. Integrated within a pyramid registration framework, the CRRM supports a multi-scale coarse-to-fine strategy based on local correlation modeling, ensuring consistent deformation-field prediction across scales. Model interpretability was further enhanced through a hierarchical visualization of the deformation fields, providing an intuitive quality assessment. Extensive experiments on brain and cardiac datasets demonstrated that CRR-Net outperforms state-of-the-art deformable registration approaches. For example, it achieved comparable performance on the LPBA40 dataset while using 32% fewer parameters and running 31% faster than CorrMLP, a representative high-performance method. This code is publicly available at <a href="https://github.com/miracledrumstick/CRR-Net">https://github.com/miracledrumstick/CRR-Net</a>.</p>

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CRR-Net: a correlation reconstruction and refinement network for deformable medical image registration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Bingxian Xie,
  • Guimei Zhang,
  • Ke Xu

摘要

Deformable image registration is a critical task in medical image analysis. However, existing learning-based methods encounter difficulties in achieving high registration accuracy under large anatomical deformations while maintaining model interpretability. To overcome these limitations, a correlation reconstruction and refinement network (CRR-Net) was introduced, representing the first framework to incorporate super-resolution reconstruction at the feature level for deformable registration. The core innovation lies in the correlation reconstruction and refinement module (CRRM), which enables precise modeling of spatial correspondences by leveraging high-resolution feature spaces. This design captures richer structural details and contextual cues while expanding the receptive field during resolution recovery, thereby improving performance in large-deformation scenarios. Integrated within a pyramid registration framework, the CRRM supports a multi-scale coarse-to-fine strategy based on local correlation modeling, ensuring consistent deformation-field prediction across scales. Model interpretability was further enhanced through a hierarchical visualization of the deformation fields, providing an intuitive quality assessment. Extensive experiments on brain and cardiac datasets demonstrated that CRR-Net outperforms state-of-the-art deformable registration approaches. For example, it achieved comparable performance on the LPBA40 dataset while using 32% fewer parameters and running 31% faster than CorrMLP, a representative high-performance method. This code is publicly available at https://github.com/miracledrumstick/CRR-Net.