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.
Figures & tables
Domain
Metric
Value
CT
μ -map MAE ↓
0.00572
PET
Whole-body SUV MAE ↓
0.0367
PET
Organ Bias (%) ↓
2.70
PET
Brain Outlier Score ↓
0.0136
Table 1: Performance on the BIC-MAC validation set across the four challenge metrics. For all metrics, lower is better ( ↓ ).
Positron emission tomography/computed tomography (PET/CT) imaging combines metabolic and anatomical information for diagnosis, treatment planning, and disease monitoring.
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 L1 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.
Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve
IDLab, ELIS, Ghent University, Belgium · Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Korea · Dept. of Mathematics, Computer Science and Statistics, Ghent University, Belgium
The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We propose GeoPACT, a geometry-anchored multimodal framework that uses NAC-PET as the spatial reference and incorporates topogram and MRI features through gated residual fusion. Absolute coordinates and whole-body conditioning support anatomically consistent patch-based prediction. Training combines attenuation-map supervision with a differentiable PET-response surrogate to reduce errors relevant to downstream PET reconstruction. Full-resolution pseudo-CT volumes are generated using sliding-window inference without requiring CT or PET labels at test time.
Xuan Loc Nguyen, Hoang-Loc Cao, Truong Thanh Hung Nguyen +4
University of Science, VNU-HCM, Ho Chi Minh City, Vietnam · Analytics Everywhere Lab, University of New Brunswick, Canada · Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
We participated in the BIC-MAC Challenge with a multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026
Rory Bell, Artemis Bouzaki, Jiaming Cao +2
Radiotherapy Related Research, Division of Cancer Sciences, School of Medical Sciences, The University of Manchester, Manchester, United Kingdom