Imaging Surgical Devices with Reduced Metal Artifact

Imaging in OR is essential to high-precision, minimally invasive spine surgery, but artifacts arising from surgical devices (e.g., implanted screws) present a major challenge to image quality. Such metal objects cause spectral shift (beam-hardening), photon starvation, and scatter, which confound visualization in regions near surgical devices – e.g. to assess the accuracy of screw placement. We present a method to predict patient and device specific orbits of C-arm cone-beam CT system that avoid metal artifacts by acquiring projection data with minimal influence from metal-related polyenergetic bias (spectral shift). The method localizes devices via neural network segmentation in a few low-dose scout views (commonly acquired for patient positioning), and all C-arm rotation and tilt angles are analyzed to identify the orbit with minimal polyenergetic bias. The method was evaluated in simulation, phantoms, and a cadaver with multiple pedicle screws, demonstrating accurate prediction of orbits that optimally avoided metal artifacts. The results yielded ~200-500 HU reduction of shading artifacts, and ~30-45% reduction in blooming artifacts about the screw shaft. Such method can improve the safety and precision of spine surgery.