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<p>This paper presents a novel framework for real-time medical image registration that synergistically combines Mamba optimization, dynamic deformation fields, and transformer-based architectures. The proposed framework addresses key challenges in the field by achieving a balance between high spatial registration accuracy and computational efficiency, while also adapting to temporal anatomical variations commonly encountered in dynamic imaging scenarios. Specifically, Mamba optimization is employed to prune redundant transformer layers and introduce adaptive learning strategies, resulting in reduced inference time and memory usage without sacrificing accuracy. Mamba, a state-space model-based transformer alternative, is used to prune redundant layers and achieve real-time efficiency. Dynamic deformation fields are introduced to provide temporal flexibility, allowing the model to adapt in real time to physiological motions such as respiration and cardiac cycles. Furthermore, the integration of multi-scale CNN encoders and transformer-based global attention enables precise spatial alignment across varying anatomical structures. We evaluate the framework on the MRI-OASIS-3 dataset, demonstrating superior performance compared to existing methods, with a Dice Similarity Coefficient (DSC) of 0.89, Normalized Cross-Correlation (NCC) of 1.00, Structural Similarity Index (SSIM) of 0.95, and Peak Signal-to-Noise Ratio (PSNR) of 35.0. The model achieves an inference time of 30&#xa0;ms and 33 FPS, validating its potential for real-time clinical deployment. These results highlight the novelty and practical significance of the proposed approach in the domain of dynamic medical image registration.</p>

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Mamba-optimized transformer framework with dynamic deformation fields for real-time medical image registration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Muhammad Kashif Jabbar,
  • Huang Jianjun,
  • Ayesha Jabbar,
  • Tariq Mahmood,
  • Sijjad Ali

摘要

This paper presents a novel framework for real-time medical image registration that synergistically combines Mamba optimization, dynamic deformation fields, and transformer-based architectures. The proposed framework addresses key challenges in the field by achieving a balance between high spatial registration accuracy and computational efficiency, while also adapting to temporal anatomical variations commonly encountered in dynamic imaging scenarios. Specifically, Mamba optimization is employed to prune redundant transformer layers and introduce adaptive learning strategies, resulting in reduced inference time and memory usage without sacrificing accuracy. Mamba, a state-space model-based transformer alternative, is used to prune redundant layers and achieve real-time efficiency. Dynamic deformation fields are introduced to provide temporal flexibility, allowing the model to adapt in real time to physiological motions such as respiration and cardiac cycles. Furthermore, the integration of multi-scale CNN encoders and transformer-based global attention enables precise spatial alignment across varying anatomical structures. We evaluate the framework on the MRI-OASIS-3 dataset, demonstrating superior performance compared to existing methods, with a Dice Similarity Coefficient (DSC) of 0.89, Normalized Cross-Correlation (NCC) of 1.00, Structural Similarity Index (SSIM) of 0.95, and Peak Signal-to-Noise Ratio (PSNR) of 35.0. The model achieves an inference time of 30 ms and 33 FPS, validating its potential for real-time clinical deployment. These results highlight the novelty and practical significance of the proposed approach in the domain of dynamic medical image registration.