Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan

2026-07-02

AI Summary

Traditional CT-based anatomical models are static and fail to capture morphological dynamics under postural changes, limiting their utility in radiographic imaging and preoperative planning. This work proposes a method to construct patient-specific dynamic digital twins from a single whole-body CT scan: by fitting the SMPL parametric human body model to obtain an aligned kinematic skeleton, segmented bones and organs are bound to an anatomy-aware rigging system, enabling pose re-targeting while preserving geometric consistency and generating digitally reconstructed radiographs (DRRs) in novel poses. To the best of our knowledge, this is the first approach capable of producing posture-adaptive anatomical models from static CT data. Experiments on three subjects demonstrate a skeleton fitting Chamfer distance of 15.8 ± 4.0 mm, bone coverage of 95.9 ± 1.8%, DRR similarity between original and reposed images with SSIM of 0.872 ± 0.016 and PSNR of 18.5 ± 1.4 dB, and stable organ coverage at 94.4 ± 0.4%.

Abstract

Patient-specific anatomical models provide individualized context for surgical planning, image-guided intervention, and algorithm development. However, most CT-derived models are static: they preserve the body configuration captured at scan time, but cannot represent how the same anatomy would appear after patient repositioning. This limitation is especially important for radiographic imaging, where appearance depends jointly on imaging geometry and patient pose. We present a proof-of-concept for constructing a patient-specific articulated digital twin from a single full-body CT scan. The method fits a parametric human body model (SMPL) to obtain a patient-aligned kinematic scaffold, binds segmented bones and organs to an anatomy-aware rig, and retargets body-pose changes while preserving skeletal geometry. On three full-body CT subjects, the fitted scaffold achieved 15.8 $\pm$ 4.0 mm chamfer distance and 95.9 $\pm$ 1.8% skeletal enclosure. Recomposition at the acquisition pose preserved major radiographic structure, with overall SSIM of 0.872 $\pm$ 0.016 and PSNR of 18.5 $\pm$ 1.4 dB across paired DRRs. Across unseen target poses, the resulting twins enabled articulation while maintaining high skeletal enclosure (94.4 $\pm$ 0.4%). As a feasibility demonstration, we render the articulated twin as pose-dependent DRRs. These results suggest the feasibility of extending static, view-controllable CT simulation toward pose-controllable anatomical twins for future synthetic imaging and positioning studies.

Problem

• patient-specific models
• articulated digital twins
• CT scan
• pose variation
• radiographic imaging

Innovation

• articulated digital twin
• patient-specific modeling
• pose retargeting
• SMPL
• digital radiography simulation