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<p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder involving multiple pathological changes. MRI and FDG-PET imaging provide complementary structural and functional information, and their combined use significantly enhances prediction accuracy. However, current multimodal algorithms struggle to capture structural abnormalities across spatial scales in MRI and localized metabolic changes in FDG-PET. Furthermore, these methods often fail to account for the heterogeneity between modalities, leading to feature conflicts or information loss during the fusion process. To address these challenges, this paper proposes a multimodal AD prediction model comprising three key components: (1) Multi-Scale Context-Aware Network (MSCA): effectively captures diverse lesion information from MRI images, enhancing sensitivity to structural abnormalities. (2) Metabolic Abnormality Focus Network (MAFN): focuses on critical metabolic regions in FDG-PET images. (3) Cross-Modal Feature Constraint Fusion Module (CMFC): CMFC integrates intra-modal feature optimization and inter-modal dynamic interactions to adaptively balance and fuse features across both modalities. This design enhances the representation capability of the fused features for lesion regions. Experimental results demonstrate that the proposed model achieves a classification accuracy of 87.02% on the AD vs. MCI vs. NC task, outperforming existing AD prediction algorithms.</p>

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Multimodal fusion network with multi-scale structure and metabolic focus for enhancing Alzheimer’s disease prediction

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Jiayuan Cheng,
  • Fei Liu,
  • Shicheng Wei

摘要

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder involving multiple pathological changes. MRI and FDG-PET imaging provide complementary structural and functional information, and their combined use significantly enhances prediction accuracy. However, current multimodal algorithms struggle to capture structural abnormalities across spatial scales in MRI and localized metabolic changes in FDG-PET. Furthermore, these methods often fail to account for the heterogeneity between modalities, leading to feature conflicts or information loss during the fusion process. To address these challenges, this paper proposes a multimodal AD prediction model comprising three key components: (1) Multi-Scale Context-Aware Network (MSCA): effectively captures diverse lesion information from MRI images, enhancing sensitivity to structural abnormalities. (2) Metabolic Abnormality Focus Network (MAFN): focuses on critical metabolic regions in FDG-PET images. (3) Cross-Modal Feature Constraint Fusion Module (CMFC): CMFC integrates intra-modal feature optimization and inter-modal dynamic interactions to adaptively balance and fuse features across both modalities. This design enhances the representation capability of the fused features for lesion regions. Experimental results demonstrate that the proposed model achieves a classification accuracy of 87.02% on the AD vs. MCI vs. NC task, outperforming existing AD prediction algorithms.