患者特异性关节式数字孪生：基于单次全身CT扫描

中文摘要 AI 辅助

患者特异性解剖模型为手术规划、图像引导干预和算法开发提供了个体化背景。然而，大多数CT衍生模型是静态的：它们保留了扫描时捕获的身体配置，但无法表示相同解剖结构在患者重新定位后的外观。这一限制对于放射学成像尤为重要，因为外观同时取决于成像几何和患者姿态。我们提出了一种概念验证，从单次全身CT扫描构建患者特异性关节式数字孪生。该方法拟合参数化人体模型（SMPL）以获得患者对齐的运动学骨架，将分割的骨骼和器官绑定到解剖感知的骨架，并在保持骨骼几何的同时重新定位身体姿态变化。在三个全身CT受试者上，拟合的骨架实现了15.8±4.0 mm的倒角距离和95.9±1.8%的骨骼包围率。在采集姿态下的重组保留了主要的放射学结构，配对DRR的整体SSIM为0.872±0.016，PSNR为18.5±1.4 dB。在未见过的目标姿态下，生成的孪生模型实现了关节运动，同时保持了高骨骼包围率（94.4±0.4%）。作为可行性演示，我们将关节式孪生模型渲染为姿态相关的DRR。这些结果表明，将静态、视角可控的CT模拟扩展到姿态可控的解剖孪生模型是可行的，可用于未来的合成成像和定位研究。

英文摘要

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 scaffold, binds segmented bones and organs to an anatomy-aware rig, and retargets body-pose changes while preserving skeletal geometry. Using full-body CT scans from three 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.

发表机构

• Johns Hopkins University（约翰霍普金斯大学）

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