Pelvic Fracture Reduction Planning via Joint Shape-Intensity Reference

Pelvic fracture reduction planning is clinically critical yet technically demanding due to the complex anatomical structure of pelvis and the topological discontinuities introduced by fractures. Existing computer-assisted planning approaches dominantly rely on shape-based models, overlooking the rich CT intensity information that is essential for accurate and patient-specific planning. To address this limitation, we propose SIRDiff, a novel framework that incorporates anatomical shape and CT intensity information to generate biomechanically plausible reference models for pelvic fracture reduction planning. SIRDiff comprises three key components: 1) the structure-aware diffusion model to reconstruct the global anatomical structure, 2) the topology-adaptive structural conditioning strategy that maps fracture landmarks into a healthy anatomical graph domain for robust structure guidance, and 3) the detail-preserved autoencoder to ensure the fine-grained image reconstruction from latent representations. Additionally, SIRDiff adopts a multi-task learning approach to jointly predict the reference CT image and corresponding bone segmentation map, which enhances its potential for clinical application and ensures better anatomical consistency. Despite being trained exclusively on synthetic fracture data, SIRDiff shows the strong generalizability to real clinical cases and consistently outperforms existing methods across multiple clinically relevant evaluation metrics, demonstrating its potential as a robust and deployable solution for pelvic fracture reduction planning.