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系统所面临的关键障碍，包括时间、成本、辐射以及工作流整合等挑战。近期，我们的研究工作X23D展示了一种仅从少量术中透视图像生成脊柱三维解剖模型的方法。该方法通过创建直接的术中解剖结构三维重建，消除了对传统基于配准的手术导航的需求。尽管取得了这些进展，但X23D的实际应用一直受到合成训练数据与真实术中图像之间领域差异的限制。为此，我们设计了一种新颖的数据采集协议，用于构建一个包含来自相同视角的合成与真实透视图像的配对数据集。利用该数据集，我们通过迁移学习改进了我们的深度学习模型，有效弥合了合成与真实X射线数据之间的领域差异。一种新颖的风格迁移机制还使我们能够将真实X射线图像转换为与合成域相似的特征，从而使我们在计算机模拟环境中训练的X23D模型能够在真实世界场景中实现高精度。我们的结果表明，改进后的模型能够仅从三张术中透视图像快速生成整个腰椎的精确三维重建。其F1分数达到84%，与我们先前基于合成数据的研究精度相当。此外，由于计算时间仅为81.1毫秒，我们的方法具备了手术集成所必需的实时能力。通过考察理想的成像设置和视角依赖性，我们进一步证实了该系统在临床环境中的实用性和可靠性。我们的研究标志着术中三维重建领域向前迈出了重要一步，为手术规划、导航和机器人技术提供了增强方案。