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arXiv · 2608.21881

Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction

Khoa Tuan Nguyen·Joris Vankerschaver·Wesley De Neve
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Abstract

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 ($\mu$) 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.

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Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve. 2026-08-22. Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction. https://arxiv.org/abs/2608.21881

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