Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis

Key points

• Here, we propose a CE-MRI synthesis method from non-contrast sequences, reducing both risk and cost.1
• Unlike existing methods that rely on channel-wise concatenation to fuse multi-sequence information for synthesis, we introduce a Mixture of Feature Difference (MoFD) module.1
• Our method successfully reconstructs tumor regions and achieves segmentation performance comparable to that obtained with real CE-MRI.1
• Our ablation studies show that although adversarial learning improves perceptual quality, a regression-based variant of our method achieves higher structural fidelity.1

Sources (1)

• [1]Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 11:24 AM

  Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors.

  Here, we propose a CE-MRI synthesis method from non-contrast sequences, reducing both risk and cost.

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