Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided procedures, such as spine instrumentation, with sparse inputs. We examine the feasibility, opportunities and challenges for imitation policy learning in bi-plane-guided cannula insertion. We develop an in silico sandbox for scalable, automated simulation of X-ray-guided spine procedures with a high degree of realism. We curate a dataset of correct trajectories and corresponding bi-planar X-ray sequences that emulate the stepwise alignment of providers. We then train imitation learning policies for planning and open-loop control that iteratively align a cannula in a vertebroplasty setting solely based on visual information. This precisely controlled setup offers insights into limitations and capabilities of this method. Our policy succeeded on the first attempt in 68.5% of cases, maintaining safe intra-pedicular trajectories across diverse vertebral levels. The policy transferred to complex anatomy, including fractures, as well as varied anatomies and initializations. Rollouts on real X-ray indicate that partial sim-to-real transfer with plausible trajectories is possible. While these preliminary results are promising, we also identify limitations, especially in entry point precision. The current results present a clear benchmark for future efforts, while with more robust priors and domain knowledge, such models may provide a foundation for future efforts toward lightweight and CT-free robotic intra-operative spinal navigation.

翻译：基于模仿学习的机器人控制策略在基于视频的机器人技术中重新受到关注。然而，该方法是否适用于稀疏输入下的X光引导手术（如脊柱内固定术）仍不明确。本研究探讨了模仿策略学习在双平面引导套管置入中的可行性、机遇与挑战。我们开发了一个高真实度的可扩展自动化X光引导脊柱手术计算机模拟沙盒，构建了包含正确轨迹及对应双平面X光序列的数据集（模拟操作者的逐步对齐过程）。随后训练了用于规划与开环控制的模仿学习策略，使其仅基于视觉信息在椎体成形术场景中迭代对齐套管。这一精确受控设置揭示了该方法的局限性及能力边界。我们的策略在68.5%的病例中首次尝试成功，并在不同椎体水平保持安全椎弓根内轨迹。该策略可迁移至复杂解剖结构（包括骨折病例）及不同解剖形态与初始位置。在真实X光下的滚动测试表明，具备合理轨迹的部分仿真到真实迁移是可行的。尽管初步结果令人鼓舞，但我们仍识别出局限性，尤其在入针点精度方面。当前结果为后续研究提供了清晰基准，通过引入更稳健的先验知识与领域知识，此类模型有望为轻量化、无需CT的机器人术中脊柱导航奠定基础。