Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan
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 \emph{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 scaffol...
| Authors | Han Zhang, Boyang Zhao, Mathias Unberath |
|---|---|
| Primary category | cs.CV |
| Published | 2 July 2026 |
| Last updated at source | 5 September 2026 |
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