Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories.
Intraoperative imaging systems are seeing an increased role in support of surgical guidance and quality assurance in the operating room for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product are often confounded by image noise and artifacts. In this work, we translated a 3D image reconstruction method (referred to as “Known-Component Reconstruction,” KC-Recon) for the first time to clinical studies with the aim of resolving both limitations. KC-Recon builds upon an optimization-based reconstruction method to reduce noise and incorporates a model of surgical instruments in the image to reduce artifacts. The first clinical pilot study involved 17 spine surgery patients imaged using the O-arm before and after spinal instrumentation. Imaging performance was evaluated in terms of low-contrast soft-tissue visibility, the ability to assess screw placement within bone margins, and the potential to image at lower radiation doses. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility. KC-Recon also yielded ~30% reduction in blooming artifact about the screw shafts and ~60% higher tissue homogeneity at the screw tips, providing clearer depiction of pedicle and vertebral body for assessment of potential breaches. Overall, the method offers a promising means to reduce patient dose in image-guided procedures, extend the use of cone-beam CT to soft-tissue surgeries, provide a valuable check against complications in the operating room (cf., post-operative CT), and serve as a basis for quantitative evaluation of quality of the surgical construct.