基于几何锚定的PET感知多模态伪CT合成用于全身衰减校正：BIC-MAC挑战
Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge
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中文总结 AI 辅助
针对无CT的PET衰减校正，提出几何锚定的多模态框架GeoPACT，融合NAC-PET、MRI和topogram，结合衰减监督与PET响应代理，实现全身伪CT合成。
中文摘要 AI 辅助
BIC-MAC挑战旨在从NAC-PET、Dixon MRI和二维topogram合成全身伪CT，以实现无CT的PET衰减校正。我们提出GeoPACT，一种基于几何锚定的多模态框架，以NAC-PET作为空间参考，并通过门控残差融合整合topogram和MRI特征。绝对坐标和全身条件支持解剖一致的基于块的预测。训练结合衰减图监督和可微分的PET响应代理，以减少与下游PET重建相关的误差。全分辨率伪CT体通过滑动窗口推理生成，测试时无需CT或PET标签。
英文摘要
The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We propose GeoPACT, a geometry-anchored multimodal framework that uses NAC-PET as the spatial reference and incorporates topogram and MRI features through gated residual fusion. Absolute coordinates and whole-body conditioning support anatomically consistent patch-based prediction. Training combines attenuation-map supervision with a differentiable PET-response surrogate to reduce errors relevant to downstream PET reconstruction. Full-resolution pseudo-CT volumes are generated using sliding-window inference without requiring CT or PET labels at test time.
发表机构
- University of Science, VNU-HCM（越南国立大学胡志明市理科大学）
- University of New Brunswick（新不伦瑞克大学）
- Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡）
机构由 AI 辅助整理，请以论文原文为准。
