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

Multimodal medical image synthesis is essential for addressing data scarcity and incomplete modality acquisition in clinical imaging. This study presents MedGAN-SSM, a framework that enhances multimodal brain MRI synthesis by integrating generative adversarial networks (GANs) with state space modeling (SSM). MedGAN-SSM uses a state space module to capture global semantic information via cross-layer transmission. A dynamic attention gate adjusts spatial and channel features, allowing the model to target relevant areas, while a multi-dimensional S6 module merges local and global features across scales to enhance synthesis quality and anatomical consistency. Experiments on the BraTS2020 and IXI datasets show that MedGAN-SSM outperforms existing methods in PSNR, SSIM, and MAE, demonstrating robustness in missing-modality scenarios while generating high-fidelity images that enhance segmentation performance. Overall, MedGAN-SSM reliably synthesizes high-quality multimodal brain MRI, preserving anatomical details while aiding automated analyses and clinical workflows in incomplete imaging conditions.