Purpose

Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error prone and involves repeated manual acquisitions or even continuous fluoroscopy. To reduce time and radiation exposure for patients and clinical staff and to avoid errors in fracture reduction or implant placement, we aim at guiding—and in the long-run automating—this procedure.

Methods

In contrast to the state of the art, we tackle this inherently ill-posed problem without requiring patient-individual prior information like preoperative computed tomography (CT) scans, without the need of registration and without requiring additional technical equipment besides the projection images themselves. We propose learning the necessary anatomical hints for efficient C-arm positioning from in silico simulations, leveraging masses of 3D CTs. Specifically, we propose a convolutional neural network regression model that predicts 5 degrees of freedom pose updates directly from a first X-ray image. The method is generalizable to different anatomical regions and standard projections.

Results

Quantitative and qualitative validation was performed for two clinical applications involving two highly dissimilar anatomies, namely the lumbar spine and the proximal femur. Starting from one initial projection, the mean absolute pose error to the desired standard pose is iteratively reduced across different anatomy-specific standard projections. Acquisitions of both hip joints on 4 cadavers allowed for an evaluation on clinical data, demonstrating that the approach generalizes without retraining.

Conclusion

Overall, the results suggest the feasibility of an efficient deep learning-based automated positioning procedure, which is trained on simulations. Our proposed 2-stage approach for C-arm positioning significantly improves accuracy on synthetic images. In addition, we demonstrated that learning based on simulations translates to acceptable performance on real X-rays.

中文翻译：

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面向骨科手术中标准投影的自动 C 臂定位。

目的

骨科手术的指导和质量控制越来越依赖于使用移动 C 臂的术中透视。在此过程中，准确获取标准化和特定解剖结构的投影至关重要。C 臂的相应迭代定位容易出错，并且涉及重复的手动采集甚至连续透视。为了减少患者和临床工作人员的时间和辐射暴露，并避免骨折复位或植入物放置中的错误，我们的目标是指导这一过程——并在长期内实现自动化。

方法

与最先进的技术相比，我们解决了这个固有的不适定问题，不需要患者个体的先验信息，如术前计算机断层扫描 (CT) 扫描，不需要配准，也不需要除了投影图像本身之外的额外技术设备。我们建议从计算机模拟中学习必要的解剖提示，以利用大量 3D CT进行有效的 C 臂定位。具体来说，我们提出了一个卷积神经网络回归模型，它可以直接从第一张 X 射线图像预测 5 个自由度的姿态更新。该方法可推广到不同的解剖区域和标准投影。

结果

对两个临床应用进行了定量和定性验证，涉及两个高度不同的解剖结构，即腰椎和股骨近端。从一个初始投影开始，对所需标准姿势的平均绝对姿势误差在不同的解剖学特定标准投影中迭代减少。在 4 具尸体上采集两个髋关节允许对临床数据进行评估，证明该方法无需再培训即可推广。

结论

总体而言，结果表明基于深度学习的高效自动定位程序的可行性，该程序经过模拟训练。我们提出的用于 C 臂定位的 2 阶段方法显着提高了合成图像的准确性。此外，我们证明了基于模拟的学习转化为真实 X 射线上可接受的性能。