Cross-modal-based VMama medical image fusion method and system combining packet ACmix convolution and selective clustering

By employing the VMamba framework with cross-modal grouping ACmix convolution and selective clustering, the problem of balancing local and global features in multimodal medical image fusion is solved, achieving efficient and accurate image fusion results. It is applicable to the diagnosis of multimodal medical images such as SPECT/MRI, PET/MRI, and CT/MRI.

CN121304644APending Publication Date: 2026-01-09THE SECOND AFFILIATED HOSPITAL OF CHONGQING MEDICAL UNIV
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Patent Information

Application Number
CN202511655197.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-12
Publication Date
2026-01-09

AI Technical Summary

Technical Problem

Existing multimodal medical image fusion methods cannot take into account both local and global features, and have high computational complexity, making it difficult to support rapid and accurate diagnosis and treatment.

Method used

We employ the VMamba framework, which combines cross-modal grouping ACmix convolution with selective clustering. Through a multi-scale pyramid structure and attention-modulated state space module, we achieve effective fusion of local and global features, reducing computational complexity and improving inference speed.

Benefits of technology

It achieves efficient multimodal medical image fusion, significantly improves the quality and texture accuracy of the fused images, enhances diagnostic accuracy, and maintains good generalization ability under low parameter complexity.

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Abstract

The invention relates to the technical field of medical image and artificial intelligence crossing, and discloses a cross-modal-based VMama medical image fusion method and system combining grouped ACmix convolution and selective clustering, and the system comprises an input module, a preprocessing module, a feature extraction module, a multi-scale fusion module, and an output reconstruction module. According to the scheme, cross-modal attention is introduced into a visual state space model for the first time, a new multi-modal image fusion framework LMACV is obtained, the framework performs linkage optimization on ACmix and VMamba structures in a cross-modal medical image for the first time, feature reconstruction efficiency is enhanced through a selective clustering mechanism, and MSE and PSNR are remarkably superior to existing methods such as MPCT, FATFusion and MATR. By fusing a convolutional network and state space modeling, the network can effectively capture local texture details, and meanwhile, long-distance semantic association is reserved; besides, linear state updating and cross-modal dynamic alignment of attention guidance are effectively realized, and compared with the latest MPCT algorithm, the fusion speed is improved by 37.5%.
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