This study tackles key obstacles in adopting surgical navigation in orthopedic surgeries, including time, cost, radiation, and workflow integration challenges. Recently, our work X23D showed an approach for generating 3D anatomical models of the spine from only a few intraoperative fluoroscopic images. This negates the need for conventional registration-based surgical navigation by creating a direct intraoperative 3D reconstruction of the anatomy. Despite these strides, the practical application of X23D has been limited by a domain gap between synthetic training data and real intraoperative images. In response, we devised a novel data collection protocol for a paired dataset consisting of synthetic and real fluoroscopic images from the same perspectives. Utilizing this dataset, we refined our deep learning model via transfer learning, effectively bridging the domain gap between synthetic and real X-ray data. A novel style transfer mechanism also allows us to convert real X-rays to mirror the synthetic domain, enabling our in-silico-trained X23D model to achieve high accuracy in real-world settings. Our results demonstrated that the refined model can rapidly generate accurate 3D reconstructions of the entire lumbar spine from as few as three intraoperative fluoroscopic shots. It achieved an 84% F1 score, matching the accuracy of our previous synthetic data-based research. Additionally, with a computational time of only 81.1 ms, our approach provides real-time capabilities essential for surgery integration. Through examining ideal imaging setups and view angle dependencies, we've further confirmed our system's practicality and dependability in clinical settings. Our research marks a significant step forward in intraoperative 3D reconstruction, offering enhancements to surgical planning, navigation, and robotics.

翻译：本研究旨在解决骨科手术中采用手术导航的关键障碍，包括时间、成本、辐射及工作流程整合等挑战。我们团队近期提出的X23D方法，仅需少量术中透视图像即可生成脊柱三维解剖模型。该方法通过直接实现术中解剖结构的三维重建，规避了传统基于配准的手术导航技术。尽管取得突破，X23D的实际应用仍受限于合成训练数据与真实术中图像之间的域差异。为此，我们设计了一种新型数据采集协议，构建了包含相同视角下合成与真实透视图像的配对数据集。基于该数据集，我们通过迁移学习优化深度学习模型，有效弥合了合成与真实X光数据之间的域差异。此外，创新的风格迁移机制可将真实X光图像转换为合成域风格，使经计算机训练的X23D模型在真实场景中实现高精度重建。实验结果表明，优化后的模型仅需三张术中透视图像即可快速生成完整腰椎的精确三维重建，F1分数达84%，与先前基于合成数据的研究精度持平。同时，模型计算耗时仅81.1毫秒，为手术集成所需的实时能力提供支撑。通过分析理想成像设置与视角依赖性，我们进一步验证了该系统在临床环境中的实用性与可靠性。本研究标志着术中三维重建的重要进展，将为手术规划、导航及机器人技术提供显著优化。