Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Abstract

Surgical targeting of the incorrect vertebral level (“wrong-level” surgery) is among the more common wrong-site surgical errors, attributed primarily to a lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. Conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error, and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (viz., CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved 10 patient CT datasets from which 50,000 simulated fluoroscopic images were generated from C-arm poses selected to approximate C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (viz., mPD < 5mm). Simulation studies showed a success rate of 99.998% (1 failure in 50,000 trials) and computation time of 4.7 sec on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.

Multi-stage 3D–2D registration for correction of anatomical deformation in image-guided spine surgery

“…The resulting transformation enables the vertebral labels in the CT image to be accurately projected and overlaid in p . The overall framework illustrated in figure 2 is consistent with that in Otake et al (2012, 2013, 2015) and De Silva et al (2016a).…”

“…Assuming a nominal magnification factor of 2, the volume was downsampled isotropically to a pix /2. The step length for ray casting was chosen to be 2 voxels (equivalently, a pix ) based on a sensitivity study reported in Otake et al (2012). Consistent with Ketcha et al (2016), a soft tissue threshold of 150 HU (shown previously to be insensitive to the particular threshold choice in the range 50 to 300 HU) was applied to the CT image (setting the value to 0 if below) to remove low attenuation regions in the forward projection, and basic overlap of the DRR with the projection image was ensured by translating the CT volume along the longitudinal direction of the patient, thereby determining an initial value for y r , and resulting in an initial registration error of ~20 – 200 mm.…”

“…The chosen SR values reflect the assumption of a coarse estimate longitudinal initialization but fairly accurate rotational initialization that comes from knowledge of patient positioning (e.g., knowing that the image is a lateral radiograph). As detailed in Otake et al (2012, 2013), a rigorous parameter sweep was performed to determine values for MS and lambda that yielded robust performance while minimizing GPU memory and computation time.…”

“…A wrong-level error can lead to suboptimal (or failed) surgical product and potentially costly litigation (Mody et al 2008). To prevent such occurrences and potentially improve workflow and confidence in target localization, a 3D-2D registration framework (called LevelCheck) has been shown to automatically overlay relevant target anatomy such as vertebral labels from 3D preoperative imaging (CT or MR) to the intraoperative 2D image as a means of decision support (Otake et al 2012, 2013, 2015, De Silva et al 2016a, b). LevelCheck has been shown to provide robust registration under many challenging scenarios that are commonly encountered in clinical images, including poor image quality and content mismatch (e.g., surgical instrumentation appearing in the radiograph that is not in the 3D image); however, the accuracy of rigid 3D-2D registration may be compromised in the presence of deformation.…”

Known-component 3D-2D registration for image guidance and quality assurance in spine surgery pedicle screw placement

“…Previous work established a method for fast, robust registration of preoperative 3D images to intraoperative radiography/fluoroscopy, 3,4 demonstrating 3D registration accuracy of target registration error (TRE) <2 mm using only two fluoroscopic views acquired at <20° separation (where 90° separation implies biplane acquisition). 5 The current work extends the registration method to iteratively solve for the rigid transformation ( ) such that a digitally reconstructed radiograph (DRR) from a known component ( C ) yields maximum similarity to intraoperative 2D projections ( f , fixed images, acquired with a C-arm).…”