Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge

Xuan Loc Nguyen; Hoang-Loc Cao; Truong Thanh Hung Nguyen; Phuc Ho; Phuc Truong Loc Nguyen; Nguyen Truong Toan To; Hung Cao

cs.CVAug 19, 2026

Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge

Authors: Xuan Loc Nguyen, Hoang-Loc Cao, Truong Thanh Hung Nguyen, Phuc Ho, Phuc Truong Loc Nguyen, Nguyen Truong Toan To, Hung Cao

Organizations: University of Science, VNU-HCM, Ho Chi Minh City, Vietnam · Analytics Everywhere Lab, University of New Brunswick, Canada · Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Abstract

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.

Figures & tables

Figure 1: Overview of GeoPACT: geometry-anchored multimodal fusion with whole-body conditioning.

Figure 2: Training objectives of GeoPACT: (a) CT attenuation supervision and (b) the full-FOV PET-response surrogate

Epochs	$\lambda_{\mu,r}$	$\lambda_{\mu,s}$	$\lambda_{\mathrm{WB}}$	$\lambda_{\mathrm{organ}}$	$\lambda_{\mathrm{brain}}$
$1$ – $30$	$1.00$	$0.50$	$0.00$	$0.00$	$0.00$
$31$ – $90$	$1.00$	$0.50$	$0.05$	$0.05$	$0.02$
$91$ – $117$	$1.00$	$0.50$	$0.15$	$0.15$	$0.05$
$118$ – $150$	$1.00$	$0.50$	$0.20$	$0.20$	$0.015$

Table 1: Loss-weight schedule. Each active component is divided by its first-100-step reference value before weighting.

Method	CT	PET
	$\mu$ -map MAE	SUV MAE	Organ Bias (%)	Brain Outlier
3D U-Net (baseline)	0.006610	0.0612	4.56	0.0541
GeoPACT (ours)	0.005821	0.0314	2.23	0.0053

Table 2: Official public-validation results on the BIC-MAC challenge. Lower is better for all metrics.

Medical Image Synthesis

Medical image synthesis creates, translates, reconstructs, and edits clinical images to fill missing data and support analysis.

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Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning

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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

Abstract

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