Source-linked AI summary
Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction
Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve
TL;DR
The paper addresses mismatches between training losses and attenuation-correction metrics, including how to use MRI information and improve reconstructed PET outcomes. It uses a metric-aligned, region-weighted μ loss, auxiliary network components, MRI inputs, line-of-response modeling, and model fusion; the submitted fusion ranks first overall on the public validation leaderboard.
Problem
Training pseudo-CT errors in HU can misprice errors relative to the CT metric, while organ activity depends on attenuation integrated along lines crossing the body rather than only within the organ.
Method
The approach retains the residual 3D U-Net backbone, adds attention gates, bottleneck self-attention, auxiliary multiscale heads, and a region-weighted L1 loss in Carney μ space inside the body mask.
Results
The submitted fusion ranks first overall with mean rank 3.50 across 26 submissions, placing second on SUV MAE and brain outlier, third on organ bias, and seventh on CT μ-MAE.
Takeaways & Limitations
The loss produced the largest single improvement, unregistered MRI achieved the best single-model CT μ-MAE, and fusion improved three non-CT metrics over its comparison blend.
Takeaways & Limitations
MuNet4 with LNGF was never trained, leaving their potentially redundant combination unmeasured.
Abstract
from arXiv · showhide
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.
1 IDLab, ELIS, Ghent University, Belgium
The paper is affiliated with Ghent University entities in Belgium and Korea, and lists model fusion and PET attenuation correction among its keywords.
- The authors are affiliated with IDLab and ELIS at Ghent University in Belgium.
- The listed keywords include model fusion, PET attenuation correction, pseudo-CT synthesis, and region-weighted loss.
1 Task and Evaluation
The task predicts whole-body pseudo-CT from NAC-PET, DIXON MRI, and a topogram, then evaluates both CT attenuation accuracy and reconstructed PET quality through a fixed pipeline.
- The input comprises NAC-PET, two-phase DIXON MRI, and a 2D topogram for whole-body pseudo-CT synthesis in HU.
- The CT µ-map MAE converts HU to linear attenuation coefficients using the Carney bilinear model before scoring.
- The HU-to-µ map has a knee near 47 HU, making equal HU errors worth 1.88 times more µ below than above the knee.
- A 4 mm FWHM Gaussian smooths the µ-map before forward projection, hiding detail finer than about 4 mm from the three AC-PET metrics while CT still sees it.
2 Method
The method combines an augmented residual 3D U-Net, metric-aligned region-weighted losses, MRI-derived signals, line-of-response supervision, and voxel-wise fusion of independently trained models.
- 2.1 Network: MuNet augments the residual 3D U-Net baseline with attention-gated skips, bottleneck self-attention, and auxiliary half- and quarter-resolution heads.
- 2.1 Network: MuNet2 reads NAC-PET and the topogram, whereas MuNet4 adds unregistered raw in- and out-phase DIXON MRI channels.
- 2.2 Lµ: a region-weighted loss in the metric’s own space: The core loss measures L1 error in Carney attenuation-coefficient µ space instead of normalized HU space, aligning optimization with the CT metric.
- 2.2 Lµ: a region-weighted loss in the metric’s own space: The loss scores only voxels inside the body mask and uses anatomical region factors, while training-only ground-truth segmentations do not add inputs at deployment.
- 2.2 Lµ: a region-weighted loss in the metric’s own space: Region weighting enables random-initialization training to learn bone, whose absence otherwise damages the skull-driven brain-outlier score.
- 2.6 LLOR: a loss along Lines Of Response: LLOR supervises µ differences along whole-body and organ-crossing lines after 4 mm smoothing, matching the spatial frequencies visible to AC-PET metrics.
- Finished independently trained models are averaged voxel-wise in HU using fixed positive weights summing to one before scoring.
3 Results
The results show that region-aware loss design and carefully selected model fusion improve complementary validation metrics, while an unmeasured MuNet4–LNGF combination remains a documented gap. The submitted fusion ranks first overall on the public validation leaderboard, despite a narrow margin.
- Training setup: 18 to 60 hours were required to train each model, depending on the schedule, using all 75 released subjects.Training used Adam with a cosine schedule and two patches per optimizer step.
- Single-model progression: 0.005679 was the best single-model CT µ-MAE, achieved when unregistered MRI was added as input channels.This result supports the usefulness of unregistered DIXON MRI as an additional input.
- Loss and regional trade-offs: 0.42 % worse on CT but 11.4 %, 15.0 % and 53.9 % better on SUV MAE, organ bias and brain outlier resulted from adding LLOR to the MRI model.LLOR was the only tested term that improved organ bias.
- Fusion refinement: 1.3 %, 1.9 % and 4.9 % improvements on SUV MAE, organ bias and brain outlier came from refining one blend member, for 0.11 % more CT µ-MAE.The training loss remained nearly unchanged, at 0.0133 versus 0.0132, so training loss alone would not have selected the refinement.
- Limitations: The results leave one table gap: MuNet4 with LNGF was never trained, although the two additions are partly redundant by construction.The authors state that they expect less from this combination but did not measure it.
- Fusion pair selection: 2.6 % below linear interpolation was the CT µ-MAE of row 8, which beat both of its members at 0.005627 versus 0.005703 and 0.005958.The gain depended on the selected pair: averaging models with similar brain failures produced little brain improvement, whereas row 8 combined complementary behavior.
- Leaderboard: 3.50 mean rank across 26 submissions placed the submitted fusion first overall on the public validation leaderboard on 16 August 2026.Its per-metric ranks were 7th on CT µ-MAE, 2nd on SUV MAE, 3rd on organ bias and 2nd on brain outlier; the second-place entry had mean rank 3.88 and beat it on three metrics.