Fast and Sliceous: Brain MRI Modality Synthesis for the BraSyn 2025 Challenge

The synthesis of missing MRI modalities has emerged as a critical strategy to address incomplete multi-parametric imaging in brain tumor diagnosis and treatment planning. Recent advances in generative models, particularly GANs and diffusion-based approaches, have shown promising results in cross-modality MRI generation, although challenges persist in preserving anatomical fidelity and minimizing synthesis artifacts. Building on the Hybrid Fusion GAN (HF-GAN) framework, several enhancements are introduced to improve synthesis quality and generalization across tumor types. These include the application of z-score normalization, optimization of network components for faster and more stable training, and the extension of the pipeline to support multi-view generation across diverse brain tumor categories such as gliomas, metastases, and meningiomas. The approach emphasizes refinement of 2D slice-based generation to ensure intra-slice coherence and reduce intensity inconsistencies, ultimately facilitating more accurate and robust tumor segmentation in scenarios with missing imaging modalities.