面向跨模态PET衰减校正的区域加权损失与模型融合
Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction
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中文总结 AI 辅助
针对BIC-MAC挑战赛，采用区域加权Carney衰减系数空间L₁损失、结合未配准DIXON MRI输入，通过两个独立模型的凸组合实现跨模态PET衰减校正，在公共验证排行榜总体排名第一。
中文摘要 AI 辅助
本文介绍了针对Big跨模态衰减校正（BIC-MAC）挑战赛的方法，该挑战赛要求从非衰减校正PET（NAC-PET）、DIXON MRI和顶片合成出以亨斯菲尔德单位表示的伪CT，并对伪CT及其重建出的衰减校正PET（AC-PET）进行评分。我们的改进基于三个思路，均超越了主办方的3D U-Net基线模型：损失函数比架构更重要，我们在CT指标本身使用的Carney衰减系数（μ）空间中计算L₁误差，并按解剖区域加权；仅在采用该损失后，未配准的DIXON MRI才能作为额外输入通道发挥作用；最后对两个独立训练的模型采用固定凸组合，在四个指标中的三个上优于单个模型，且在公共验证排行榜上总体排名第一。
英文摘要
We describe our approach to the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge, which asks for a pseudo-CT in Hounsfield Units to be synthesized from Non-Attenuation-Corrected PET (NAC-PET), DIXON MRI and a topogram, and scores both the pseudo-CT and the Attenuation-Corrected PET (AC-PET) reconstructed from it. Three ideas carried our improvements over the organizers' 3D U-Net baseline. The loss matters more than the architecture: we compute the $L_1$ error in the Carney attenuation-coefficient ($μ$) space that the CT metric itself uses, weighted by anatomical region. Only once that loss was in place did the unregistered DIXON MRI work as extra input channels. A fixed convex combination of two independently trained models then beat both of its members on three of the four metrics and ranks first overall on the public validation leaderboard.
发表机构
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus（根特大学全球校区生物系统与生物技术数据科学中心）
- Ghent University（根特大学）
机构由 AI 辅助整理，请以论文原文为准。
