CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
For medical imaging researchers and clinicians, this work provides a practical way to improve deformable registration across rigid and soft tissues in whole-body PET, though the approach is incremental.
The paper addresses the challenge of anatomical heterogeneity in whole-body PET registration by introducing a CT-guided spatially-varying regularization strategy that adapts regularization strength per voxel. On a clinical dataset of 296 patients, the method achieves statistically significant improvements in whole-body registration and organ-wise alignment over the weakly-supervised baseline.
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.