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.
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.
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.
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.
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We thank Mathias Unberath and Cong Gao for their insights into DeepDRR. Moreover, we thank Rimasys for facilitating the validation on cadaver data. This work was partially funded by Siemens Healthcare GmbH, Erlangen, Germany.
Conflict of interest
The authors declare that they have no conflict of interest.
The data were obtained retrospectively from anonymized databases and not generated intentionally for the study. For this type of study, formal consent is not required.
The acquisition of data from living patients had a medical indication, and informed consent was not required. The acquired datasets of cadavers were available retrospectively after they had been generated during surgical courses for physicians. The corresponding consent for body donation for these purposes has been obtained.
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Kausch, L., Thomas, S., Kunze, H. et al. Toward automatic C-arm positioning for standard projections in orthopedic surgery. Int J CARS 15, 1095–1105 (2020). https://doi.org/10.1007/s11548-020-02204-0
- Pose estimation
- Fluoroscopic imaging
- C-arm positioning
- Standard projection