用多模态信息生成伪CT，提升PET衰减校正精度

Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning

• 分通道编码PET与MRI，融合多尺度特征
• 通过顶部图条件控制瓶颈层，提升跨模态对齐
• 减少对体素级配准依赖，适合多模态数据融合

我们参与了BIC-MAC挑战，提出一种基于3D块的多模态U-Net模型，从无衰减PET（NAC-PET）、MRI和2D顶部图生成伪CT。通过使用独立的PET与MR编码器、多尺度特征融合以及在瓶颈层采用FiLM机制进行顶部图条件控制，模型有效整合了跨模态互补信息，同时降低了对模态间精确体素级对应关系的依赖。最终提交结果见：https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026

原文摘要 · Abstract (English)

We participated in the BIC-MAC Challenge with a multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026

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