Topogram门控多模态伪CT合成用于PET/MR衰减校正
Topogram-Gated Multi-Modal Pseudo-CT Synthesis for PET/MR Attenuation Correction
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中文总结 AI 辅助
本研究提出Topogram门控多模态残差3D U-Net，融合NAC-PET、Dixon MRI和topogram图像合成伪CT，用于PET/MR衰减校正，在BIC-MAC验证集上取得低MAE和器官偏差。
中文摘要 AI 辅助
作为大跨模态衰减校正挑战赛（BIC-MAC）的一部分，我们旨在从全身非衰减校正PET（NAC-PET）、Dixon MRI和二维topogram图像合成用于PET/MR衰减校正的伪CT图像。我们开发了一个残差3D U-Net，其中专用的2D topogram编码器提供有界的多尺度特征门控。该网络在CT和511-keV衰减图空间中联合监督训练。两个互补模型——一个强调活动和颅脑区域，另一个包含投影域衰减校正因子正则化器——通过在CT空间中取平均进行组合。在受控开发实验中，这两个训练目标产生了互补的误差分布。在在线BIC-MAC验证排行榜上，该集成模型实现了mumap MAE为0.005656，全身SUV MAE为0.0356，器官偏差为2.65%，脑部异常值评分为0.0294。Topogram门控多模态学习将局部体积信息与全局投影解剖结构相结合，为PET/MR伪CT合成提供了一种实用且物理信息丰富的方法。
英文摘要
As part of the Big Cross-Modal Attenuation Correction Challenge (BIC-MAC), we aimed to synthesise pseudo-CT images for PET/MR attenuation correction from whole-body non-attenuation-corrected PET (NAC-PET), Dixon MRI, and a two-dimensional topogram image. We developed a residual 3D U-Net in which a dedicated 2D topogram encoder provides bounded, multi-scale feature gating. The network was supervised jointly in CT and 511-keV attenuation-map space. Two complementary models, one emphasizing activity and cranial regions and one including a projection-domain attenuation-correction-factor regulariser, were combined by equal averaging in CT space. In controlled development experiments, the two training objectives yielded complementary error profiles. On the online BIC-MAC validation leaderboard, the ensemble achieved a mumap MAE of 0.005656, whole-body SUV MAE of 0.0356, organ bias of 2.65%, and brain outlier score of 0.0294. Topogram-gated multi-modal learning combines local volumetric information with global projected anatomy and provides a practical, physically informed approach to pseudo-CT synthesis for PET/MR.
发表机构
- Vrije Universiteit Brussel（布鲁塞尔自由大学）
- imec（imec研究院）
- Research Foundation - Flanders（弗兰德斯研究基金会）
- Université catholique de Louvain（天主教鲁汶大学）
- Nuclivision BV（Nuclivision公司）
- Universitair Ziekenhuis Brussel（布鲁塞尔大学医院）
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