Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

中文翻译：

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骨盆骨折手术计划中骨折模拟和基于变形的恢复预测的双向框架


骨盆骨折是一种严重的创伤，会危及生命。手术复位对于恢复骨盆的解剖结构和功能完整性至关重要，需要准确的术前计划。然而，骨盆骨折的复杂性和有限的数据可用性使得在临床环境中需要进行劳动密集型的手动矫正。我们在本文中描述了一种基于骨折模拟和结构恢复的自动骨盆骨折手术计划的新型双向框架。我们的骨折模拟方法考虑了患者特定的骨盆结构、骨密度信息和骨折的随机性，能够从健康骨盆生成各种类型的骨折病例。基于这些特征和对抗性学习，我们开发了一种新颖的结构恢复网络来预测骨折前后 CT 图像中的变形映射，以实现任何骨折的精确结构重建。此外，开发了一种基于骨盆解剖对称先验的自我监督策略，以优化恢复的骨盆结构的细节。最后，恢复的骨盆被用作模板来生成手术缩小计划，其中碎片以有效的拼图注册方式重新定位。对模拟和临床数据集（包括金属伪影扫描）的大量实验表明，我们的方法实现了良好的准确性和鲁棒性：修复体的平均 SSIM 为 90.7%，真实数据集中减少的平移误差为 2.88 毫米，旋转误差为 3.18° 。我们的方法需要 52.9 秒才能完成模型研究中的手术计划，与标准临床工作流程相比显着加快。 我们的方法可以促进在临床环境中针对个体患者制定有效的骨盆骨折手术计划。