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arXiv · 2604.22905

CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration

Xiangcen Wu·Ruohua Chen·Sichun Li·Qianye Yang·Sheng Liu·Jianjun Liu·Zhaoheng Xie
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Abstract

Whole-body Positron Emission Tomography (PET) registration is essential for multi-parametric tumor characterization and assessment of metastatic disease progression. In deep learning-based deformable registration, the dense displacement field (DDF) regularizer is crucial for stabilizing optimization and preventing unrealistic deformations in large 3D volumes. A key challenge in whole-body deformable registration is anatomical heterogeneity, rigid structures (e.g., bones) should undergo stronger regularization, whereas soft tissues require more flexible deformation and weaker constraints. In this work, we propose a simple yet effective CT-guided spatially-varying regularization strategy for whole-body cross-tracer deformable PET registration. The key idea is to use the paired CT volume from the PET/CT acquisition to construct a voxel-wise regularization map for the DDF, replacing the conventional single global regularization weight. This yields anatomy-adaptive regularization strength across rigid and soft tissues. The proposed method is evaluated on a real clinical cross-tracer PET/CT dataset of 296 patients involving 18F-PSMA and 18F-FDG, showing that the proposed method achieves statistically significant improvements over weakly-supervised registration baseline in both whole-body registration performance and organ-wise alignment.

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Xiangcen Wu, Ruohua Chen, Sichun Li, Qianye Yang, Sheng Liu, Jianjun Liu, Zhaoheng Xie. 2026-04-24. CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration. https://arxiv.org/abs/2604.22905

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