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This study introduces a method for creating patient-specific articulated digital twins from a single full-body CT scan, addressing the limitation of static anatomical models that cannot adapt to patient repositioning. By fitting a parametric human body model (SMPL) and binding segmented anatomical structures to a kinematic scaffold, the researchers achieved high accuracy in skeletal enclosure and radiographic structure preservation across various poses. The articulated digital twins demonstrated a significant ability to maintain anatomical fidelity while allowing for pose-dependent articulation, paving the way for enhanced surgical planning and imaging applications.
Articulated digital twins from a single CT scan can adapt to patient repositioning, revolutionizing surgical planning and imaging fidelity.
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.
