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 the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The 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 (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the 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 (namely mPD <5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s 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 the 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.

Figure 1

Overview of the proposed system.

Figure 2

Workflow diagram of the proposed method showing input data (preop CT data with labels, geometric calibration, and intraoperative fluoroscopy) and two main components of the 3D-2D “LevelCheck” registration approach – an initial 2DOF search and an iterative 6DOF optimization.

Figure 3

Clinical CT datasets used in the simulation study. Each image was randomly selected from the NCI TCIA database, providing a fairly broad range in body habitus and normal anatomical variations.

Figure 4

Histogram of randomly generated 6DOF C-arm pose parameters. A transformation computed from these parameters was used as an offset to the nominal AP position to simulate operator variability in C-arm positioning for fluoroscopy. Larger distributions were assumed for translations in Y and Z, since uncertainty along these axes is generally larger than the others.

Figure 5

Setup for experiments using real fluoroscopy images of an anthropomorphic phantom. (a) Mobile C-arm, (b) photograph of the phantom, and (c) example fluoroscopic image from the nominal AP view. Fluoroscopic images throughout are displayed in a “black-bone” grayscale typical of fluoroscopic displays.

Figure 6

Evaluation of geometric error in vertebral localization. (a) Estimated vertebral levels (yellow) as labeled by LevelCheck on a real fluoroscopic image. (b) Error in vertebral localization was characterized in terms of the mean projection distance (mPD) between estimated (yellow) and true (cyan) locations in the 2D image plane.

Figure 7

Initial 2DOF search. (a) DRRs generated from preoperative CT data with varying X and Y translation (10 mm interval) from the nominal AP position. The top row shows target fluoroscopy images for four examples at various (true) C-arm poses, and the plot below shows the GI computed as a function of X and Y. (b) Example at the nominal AP pose. (c) Example with 40 mm translation in X. (d) Example with X translation and θ_z rotation. (e) Example with a combination of translations and rotations. The more complex poses reduce the magnitude of the GI peak but retain a global maximum at the true pose.

Figure 8

(a) Success rate and computation time for various settings of CMA-ES population size (λ). (b) Distribution of the number of failures in each of 5,000 trials in each of the 10 CT datasets. (c) Success rate increased asymptotically with λ and approached unity at population size of 120.

Figure 9

Convergence plots of a typical registration trial. (a) Translational coordinates of the pose estimate versus iteration number. (b) Rotational coordinates of the pose estimate versus iteration number. (c) Similarity metric plotted versus iteration number, where (GI_max–GI) denotes the difference between GI and its maximum value, GI_max. (d) Mean projection distance computed versus iteration number. The plots show that mPD reduced to < ~1 mm in ~1000–2000 iterations (which was well within requirements of the spine level labeling application) and could be reduced to still finer levels of accuracy with more iterations.

Figure 10

Summary of registration accuracy in simulation studies. Translation errors (left axis) and rotation errors (right axis) from the nominal AP position are shown for cases: (a) before registration, (b) after initial 2DOF search, and (c) after 6DOF iterative optimization search. (d) The mean projection distance before registration, after initial search, and after optimization. The LevelCheck registration was accurate to within ~1 mm overall, with a single failure case for which mPD exceeded 5 mm.

Figure 11

LevelCheck registration performance in real fluoroscopy. (a) Mean projection distance for images acquired at various C-arm angles, θ, about the nominal AP view (b) Fluoroscopy images acquired at θ = −35°, −20°, 0°, 20°, 35° overlaid with the estimated label for each spine level (all of which were within 1 mm of the true level location).

Figure 12

An example failure case. The image corresponds to the single failure (1/50,000) observed for the CMA-ES optimizer parameter setting λ=120. The overlay of estimated level labels (yellow) are seen to be displaced from the true levels (cyan) by one vertebra.

Figure 13

One slice of the search space in the failed registration shown in figure 12. The plot shows the GI similarity metric between the fluoroscopy and DRRs computed at a pose T(α)=T_est+α(T_true−T_est), where T_est and T_true are the estimated and true pose, respectively (see 2.4. for detail). The shallow local optimum around α=0 caused the optimization to converge at the wrong pose for smaller values of CMA-ES population size.

Figure 14

An example clinical application scenario in pedicle screw placement. Vertebral labels and the planned trajectories defined in the preoperative CT were automatically overlaid onto the simulated fluoroscopy images using the LevelCheck algorithm, (a) AP view and (b) LAT view with LevelCheck labels overlaid in yellow. The algorithm was robust against the presence of screws in fluoroscopy which were present in preoperative CT.