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Abstract <p>&#xa0;Multi-modal medical image registration aims to align images from different modalities to establish spatial correspondences. Although deep learning-based methods have shown great potential, the lack of explicit reference relations makes unsupervised multi-modal registration still a challenging task. In this paper, we propose a novel unsupervised dual-stream multi-modal registration framework (DSMR), which combines a dual-stream registration network with a refinement module. Unlike existing methods that treat multi-modal registration as a uni-modal problem using a translation network, DSMR leverages the moving, fixed and translated images to generate two deformation fields. Specifically, we first utilize a translation network to convert a moving image into a translated image similar to a fixed image. Then, we employ the dual-stream registration network to compute two deformation fields respectively: the initial deformation field generated from the fixed image and the moving image, and the translated deformation field generated from the translated image and the fixed image. The translated deformation field acts as a pseudo-ground truth to refine the initial deformation field and mitigate issues such as artificial features introduced by translation. Finally, we use the refinement module to enhance the deformation field by integrating registration errors and contextual information. Extensive experimental results show that our DSMR achieves exceptional performance, demonstrating its strong generalization in learning the spatial relationships between images from unsupervised modalities. The source code of this work is available at <a href="https://github.com/raylihaut/DSMR">https://github.com/raylihaut/DSMR</a>.</p> Graphic Abstract <p>Illustration of the proposed DSMR. After being processed by the translation network, the moving image is converted into a translated image. Subsequently, the moving image, the translated image and the fixed image are input into the registration architecture. Then the refinement module further refines the obtained deformation field to get the final deformation field, and further obtains the registered image.</p>

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DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Lei Li,
  • Liumin Zhu,
  • Qifu Wang,
  • Zhuoli Dong,
  • Tianli Liao,
  • Peng Li

摘要

Abstract

 Multi-modal medical image registration aims to align images from different modalities to establish spatial correspondences. Although deep learning-based methods have shown great potential, the lack of explicit reference relations makes unsupervised multi-modal registration still a challenging task. In this paper, we propose a novel unsupervised dual-stream multi-modal registration framework (DSMR), which combines a dual-stream registration network with a refinement module. Unlike existing methods that treat multi-modal registration as a uni-modal problem using a translation network, DSMR leverages the moving, fixed and translated images to generate two deformation fields. Specifically, we first utilize a translation network to convert a moving image into a translated image similar to a fixed image. Then, we employ the dual-stream registration network to compute two deformation fields respectively: the initial deformation field generated from the fixed image and the moving image, and the translated deformation field generated from the translated image and the fixed image. The translated deformation field acts as a pseudo-ground truth to refine the initial deformation field and mitigate issues such as artificial features introduced by translation. Finally, we use the refinement module to enhance the deformation field by integrating registration errors and contextual information. Extensive experimental results show that our DSMR achieves exceptional performance, demonstrating its strong generalization in learning the spatial relationships between images from unsupervised modalities. The source code of this work is available at https://github.com/raylihaut/DSMR.

Graphic Abstract

Illustration of the proposed DSMR. After being processed by the translation network, the moving image is converted into a translated image. Subsequently, the moving image, the translated image and the fixed image are input into the registration architecture. Then the refinement module further refines the obtained deformation field to get the final deformation field, and further obtains the registered image.