Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data

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 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.