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MedGAN-SSM: Multimodal brain image synthesis network integration using SSM empowered GAN.

Tianming Song1, Mingzhi Wang2, Zhe Ren1

  • 1School of Integrated Circuit, Wuxi Vocational College of Science and Technology, Wuxi 214028, China.

Iscience
|May 25, 2026
PubMed
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This study introduces MedGAN-SSM, a novel framework for high-quality multimodal brain MRI synthesis. It effectively addresses data scarcity and missing modalities, improving automated analysis in clinical settings.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Multimodal medical image synthesis is crucial for overcoming data limitations in clinical settings.
  • Existing methods struggle with data scarcity and incomplete imaging modalities.
  • High-fidelity synthesis is needed to support downstream tasks like segmentation.

Purpose of the Study:

  • To present MedGAN-SSM, a framework enhancing multimodal brain MRI synthesis.
  • To improve image quality and anatomical consistency in generated MRIs.
  • To demonstrate robustness in scenarios with missing imaging modalities.

Main Methods:

  • Integration of generative adversarial networks (GANs) with state space modeling (SSM).
  • Utilizing a state space module for global semantic information capture via cross-layer transmission.
Keywords:
Computational bioinformaticsNeuroscience

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  • Employing a dynamic attention gate for feature adjustment and an S6 module for multi-scale feature fusion.

    • Main Results:

    • MedGAN-SSM outperforms existing methods on BraTS2020 and IXI datasets in PSNR, SSIM, and MAE.
    • The framework shows robustness in missing-modality scenarios.
    • Generated high-fidelity images that improved segmentation performance.

    Conclusions:

    • MedGAN-SSM reliably synthesizes high-quality multimodal brain MRI, preserving anatomical details.
    • The framework aids automated analyses and clinical workflows, especially under incomplete imaging conditions.
    • This approach offers a robust solution for data augmentation and missing data imputation in medical imaging.