用于无CT的PET衰减校正的解剖学与物理学监督：BIC-MAC 2026挑战赛
Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge
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中文总结 AI 辅助
本研究针对BIC-MAC 2026挑战赛，基于nnU-Net架构结合解剖学与物理学监督，利用预训练权重优化流程，实现了无CT的PET衰减校正方案，验证了该方法的有效性。
中文摘要 AI 辅助
本报告介绍了我们向2026年跨模态衰减校正挑战赛（Big Cross-Modal Attenuation Correction, BIC-MAC）提交的方案，旨在通过多模态伪CT合成实现无CT的PET衰减校正。我们基于标准的nnU-Net架构，结合解剖学与物理学监督，以提升伪CT质量及下游PET重建效果。解剖学监督通过冻结的TotalSegmentator特征提取器、解剖学引导的结构约束与补丁采样实现；物理学监督则基于多角度衰减投影，采用可微衰减校正因子投影损失达成。此外，网络通过在SynthRAD挑战赛的MR-to-CT数据集上训练获得的预训练权重初始化，仅做了极少的架构修改，同时在nnU-Net流程的各组件（包括预处理、计划与监督设计等）中追求性能提升。我们的最终提交方案验证了结合解剖学监督、衰减物理学及高效nnU-Net缩放对无CT的PET衰减校正的有效性。
英文摘要
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.
发表机构
- National Technical University of Athens（雅典国家技术大学）
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