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

TL;DR

This paper introduces a CT-guided spatially-varying regularization method for deformable PET registration that adapts regularization strength based on anatomy, improving alignment accuracy across tissues.

Contribution

The authors propose a voxel-wise regularization map derived from CT data to enhance deformable PET registration, addressing tissue heterogeneity challenges.

Findings

Significant improvement in whole-body registration accuracy.

Enhanced organ-wise alignment compared to baseline methods.

Validated on a clinical dataset of 296 patients.

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.