AGENTIC-SYSTEMSApril 3, 20261 min read18 views

Brain MR Image Synthesis with Multi-contrast Self-attention GAN

Researchers have developed 3D-MC-SAGAN, a unified AI framework capable of synthesizing missing MRI modalities from a single T2 input while preserving critical tumor characteristics for neuro-oncological assessment.

Electrical Engineering and Systems Science > Image and Video Processing

Title:Brain MR Image Synthesis with Multi-contrast Self-attention GAN

Accurate and complete multi-modal Magnetic Resonance Imaging (MRI) is essential for neuro-oncological assessment, as each contrast provides complementary anatomical and pathological information. However, acquiring all modalities (e.g., T1c, T1n, T2, T2f) for every patient is often impractical due to time, cost, and patient discomfort, potentially limiting comprehensive tumour evaluation. We propose 3D-MC-SAGAN (3D Multi-Contrast Self-Attention generative adversarial network), a unified 3D multi-contrast synthesis framework that generates high-fidelity missing modalities from a single T2 input while explicitly preserving tumour characteristics. The model employs a multi-scale 3D encoder-decoder generator with residual connections and a novel Memory-Bounded Hybrid Attention (MBHA) block to capture long-range dependencies efficiently, and is trained with a WGAN-GP critic and an auxiliary contrast-conditioning branch to produce T2f, T1n, and T1c volumes within a single unified network. A frozen 3D U-Net-based segmentation module introduces a segmentation-consistency constraint to preserve lesion morphology. The composite objective integrates adversarial, reconstruction, perceptual, structural similarity, contrast-classification, and segmentation-guided losses to align global realism with tumour-preserving structure. Extensive evaluation on 3D brain MRI datasets demonstrates that 3D-MC-SAGAN achieves state-of-the-art quantitative performance and generates visually coherent, anatomically plausible contrasts with improved distribution-level realism. Moreover, it maintains tumour segmentation accuracy comparable to fully acquired multi-modal inputs, highlighting its potential to reduce acquisition burden while preserving clinically meaningful information.

Source: arXiv cs.AI Recent

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