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

2026-04-24

AI Summary

This study addresses the challenge of imposing differential deformation constraints for rigid structures and soft tissues in whole-body PET image registration by proposing a CT-guided spatially varying regularization strategy. The method leverages paired CT images to generate voxel-wise regularization maps that adaptively modulate the strength of regularization according to local anatomical structure, replacing the conventional global uniform weight. Implemented within a deep learning-based weakly supervised deformable registration framework, the approach demonstrates significant improvements over existing baselines on a clinical dataset comprising 296 cases of 18F-PSMA and 18F-FDG PET/CT scans, achieving enhanced registration accuracy at both whole-body and organ-specific levels.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Structured Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• whole-body PET registration
• anatomical heterogeneity
• spatially-varying regularization
• deformable registration
• voxel-wise regularization

Innovation

• spatially-varying regularization
• CT-guided
• voxel-wise deformable registration
• whole-body PET
• dense displacement field