Abstract
Trauma and orthopedic surgeries that involve fluoroscopic guidance crucially depend on the acquisition of correct anatomy-specific standard projections for monitoring and evaluating the surgical result. This implies repeated acquisitions or even continuous fluoroscopy. To reduce radiation exposure and time, we propose to automate this procedure and estimate the C-arm pose update directly from a first X-ray without the need for a pre-operative computed tomography scan (CT) or additional technical equipment. Our method is trained on digitally reconstructed radiographs (DRRs) which uniquely provide ground truth labels for arbitrary many training examples. The simulated images are complemented with automatically generated segmentations, landmarks, as well as a k-wire and screw simulation. To successfully achieve a transfer from simulated to real X-rays, and also to increase the interpretability of results, the pipeline was designed by closely reflecting on the actual clinical decision-making of spinal neurosurgeons. It explicitly incorporates steps like region-of-interest (ROI) localization, detection of relevant and view-independent landmarks, and subsequent pose regression. To validate the method on real X-rays, we performed a large specimen study with and without implants (i.e. k-wires and screws). The proposed procedure obtained superior C-arm positioning accuracy (\(p_{wilcoxon}\ll 0.01\)), robustness, and generalization capabilities compared to the state-of-the-art direct pose regression framework.
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Kausch, L. et al. (2021). C-Arm Positioning for Spinal Standard Projections in Different Intra-operative Settings. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_34
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