用解剖与物理双重监督，提升无CT PET图像校正精度。
Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge
- 结合解剖结构与衰减物理规律，指导伪CT生成。
- 在BIC-MAC挑战中实现更高质量的伪CT与PET重建。
- 基于nnU-Net轻量改进，适合医学影像跨模态任务。
本文报告了我们在2026年大跨模态衰减校正（BIC-MAC）挑战中，针对无CT PET衰减校正所提交的方法。通过多模态伪CT合成，我们基于标准nnU-Net架构，引入解剖与物理双重监督以提升伪CT质量及下游PET重建效果。解剖监督采用冻结的TotalSegmentator特征提取器、解剖引导的结构约束和局部块采样；物理监督则基于多角度衰减投影的可微分衰减校正因子投影损失。网络使用SynthRAD挑战中MR-to-CT预训练权重初始化。仅进行少量架构修改，但在预处理、训练计划、监督设计等环节全面优化。最终结果验证了结合解剖信息、衰减物理机制与高效nnU-Net扩展在无CT PET校正中的有效性。
原文摘要 · Abstract (English)
This report describes our submission to the Big Cross-Modal Attenuation Correction (BIC-MAC) 2026 Challenge for CT-less PET attenuation correction through multimodal pseudo-CT synthesis. We build upon a standard nnU-Net architecture and combine anatomical and physical supervision to improve both pseudo-CT quality and downstream PET reconstruction. Anatomical supervision is introduced through a frozen TotalSegmentator feature extractor, anatomy-guided structural constraints and patch sampling, while physical supervision is achieved using a differentiable attenuation correction factor projection loss based on multi-angle attenuation projections. Furthermore, the network is initialized with pretrained weights obtained from training on the SynthRAD Challenge MR-to-CT dataset. Minimal architectural modifications are applied, while performance improvements are pursued across the nnU-Net pipeline, including preprocessing, plans, and supervision design, among other components. Our final submission demonstrates the effectiveness of combining anatomical supervision, attenuation physics, and efficient nnU-Net scaling for CT-less PET attenuation correction.
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