Metal artifact reduction (MAR) algorithms are used with cone beam computed tomography (CBCT) during augmented reality surgical navigation for minimally invasive pedicle screw instrumentation. The aim of this study was to assess intra- and inter-observer reliability of pedicle screw placement and to compare the perception of baseline image quality (NoMAR) with optimized image quality (MAR). CBCT images of 24 patients operated on for degenerative spondylolisthesis using minimally invasive lumbar fusion were analyzed retrospectively. Images were treated using NoMAR and MAR by an engineer, thus creating 48 randomized files, which were then independently analyzed by 3 spine surgeons and 3 radiologists. The Gertzbein and Robins classification was used for screw accuracy rating, and an image quality scale rated the clarity of pedicle screw and bony landmark depiction. Intra-class correlation coefficients (ICC) were calculated. NoMAR and MAR led to similarly good intra-observer (ICC > 0.6) and excellent inter-observer (ICC > 0.8) assessment reliability of pedicle screw placement accuracy. The image quality scale showed more variability in individual image perception between spine surgeons and radiologists (ICC range 0.51–0.91). This study indicates that intraoperative screw positioning can be reliably assessed on CBCT for augmented reality surgical navigation when using optimized image quality. Subjective image quality was rated slightly superior for MAR compared to NoMAR.

Angiographic projections of the left atrium (LA) and the pulmonary veins (PV) acquired with a rotational C-arm system are used for 3D image reconstruction and subsequent automatic segmentation of the LA and PV to be used as roadmap in fluoroscopy guided LA ablation procedures. Acquisition of projections at high oblique angulations may be problematic due to increased collision danger of the detector with the right shoulder of the patient. We investigate the accuracy of image reconstruction and model based roadmap segmentation using limited angle C-arm tomography. The reduction of the angular range from 200°to 150°leads only to a moderate increase of the segmentation error from 1.5 mm to 2.0 mm if matched conditions are used in the segmentation, i.e. the model based segmentation is trained on images reconstructed with the same angular range as the test images. The minor decrease in accuracy may be outweighed by clinical workflow improvement, gained when large C-arm angulations can be avoided.