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Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation.

Tianran Li, Marius Staring, Yuchuan Qiao

    IEEE Transactions on Medical Imaging
    |November 7, 2025
    PubMed
    Summary

    This study introduces a Recurrent Correlation-based framework for deformable image registration, improving efficiency and accuracy in medical imaging. The novel method effectively handles large deformations with reduced computational cost.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Deformable image registration is crucial for medical imaging analysis.
    • Deep learning methods accelerate registration but struggle with large deformations.
    • Current methods like voxel-to-region matching lack long-range correspondence modeling.

    Purpose of the Study:

    • To develop an efficient deep learning framework for deformable image registration capable of handling large deformations.
    • To improve the accuracy and reduce the computational cost of medical image registration.

    Main Methods:

    • Proposed a Recurrent Correlation-based framework with dynamic region relocation for matching.
    • Employed a lightweight recurrent update module with memory and decoupled motion/texture features.
    • Conducted experiments on brain MRI and abdominal CT datasets with and without affine pre-registration.

    Main Results:

    • The Recurrent Correlation-based framework demonstrated a strong accuracy-computation trade-off.
    • Achieved comparable performance to state-of-the-art methods on the OASIS dataset.
    • Utilized only 9.5% of FLOPs and ran 96% faster than the RDP method.

    Conclusions:

    • The proposed framework efficiently handles large deformations in medical image registration.
    • Offers a superior balance between accuracy and computational efficiency compared to existing methods.
    • Shows significant potential for advancing medical image analysis applications.