Jonathan Boisvert

Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models

This paper introduces a method to analyze the variability of the spine shape and of the spine shape deformations using articulated shape models. The spine shape was expressed as a vector of relative poses between local coordinate systems of neighboring vertebrae. Spine shape deformations were then modeled by a vector of rigid transformations that transforms one spine shape into another. Because rigid transformations do not naturally belong to a vector space, conventional mean and covariance could not be applied. The Frechet mean and a generalized covariance were used instead. The spine shapes of a group of 295 scoliotic patients were quantitatively analyzed as well as the spine shape deformations associated with the Cotrel-Dubousset corrective surgery (33 patients), the Boston brace (39 patients), and the scoliosis progression without treatment (26 patients). The variability of intervertebral poses was found to be inhomogeneous (lumbar vertebrae were more variable than the thoracic ones) and anisotropic (with maximal rotational variability around the coronal axis and maximal translational variability along the axial direction). Finally, brace and surgery were found to have a significant effect on the Frechet mean and on the generalized covariance in specific spine regions where treatments modified the spine shape.

Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine

We present a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions. In addition, we introduce a comprehensive validation scheme that accounts for changes in the curvature of the spine between preoperative and intraoperative imaging. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. During each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric is calculated with the real US image. Validation studies are performed on CT and US images from a sheep cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging. For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the sheep cadaver with soft-tissue information was 87%. The results demonstrate that our algorithm allows for robust registration of US and CT datasets, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions.

Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model

This paper proposes a novel method for fast 3D reconstructions of the scoliotic spine from two planar radiographs. The method uses a statistical model of the shape of the spine for computing the 3D reconstruction that best matches the user input (about 7 control points per radiograph). In addition, the spine was modelled as an articulated structure to take advantage of the dependencies between adjacent vertebrae in terms of location, orientation and shape. The accuracy of the method was assessed for a total of 30 patients with mild to severe scoliosis (Cobb angle [22°, 70°]) by comparison with a previous validated method. Reconstruction time was 90 s for mild patients, and 110 s for severe. Results show an accuracy of ∼0.5mm locating vertebrae, while orientation accuracy was up to 1.5° for all except axial rotation (3.3° on moderate and 4.4° on severe cases). Clinical indices presented no significant differences to the reference method (Wilcoxon test, p ≤ 0.05) on patients with moderate scoliosis. Significant differences were found for two of the five indices (p=0.03) on the severe cases, while errors remain within the inter-observer variability of the reference method. Comparison with state-of-the-art methods shows that the method proposed here generally achieves superior accuracy while requiring less reconstruction time, making it especially appealing for clinical routine use.

Articulated Spine Models for 3-D Reconstruction From Partial Radiographic Data

Three-dimensional models of the spine are extremely important to the assessment of spinal deformities. However, it could be difficult in practical situations to obtain enough accurate information to reconstruct complete 3-D models. This paper presents a set of methods to rebuild complete models either from partial 3-D models or from 2-D landmarks. The spine was modeled as an articulated object to take advantage of its natural anatomical variability. A statistical model of the vertebrae and spine shape was first derived. Then, complete models were computed by finding the articulated spine descriptions that were consistent with the observations while optimizing the prior probability given by the statistical model. The observations used were the absolute positions, orientations, and shapes of the vertebrae when a partial 3-D model was available. The reconstruction of 3-D spine models from 2-D landmarks identified on radiograph(s) was performed by minimizing the Mahalanobis distance and the landmarks reprojection error. The vertebrae estimated from partial models were within 2 mm of the measured values (which is comparable to the accuracy of currently used methods) if at least 25% of the vertebrae were available. Experiments also suggest that the reconstruction from posterior-anterior and lateral radiographs using the proposed method is more accurate than the conventional triangulation method.

Registration of a statistical shape model of the lumbar spine to 3D ultrasound images

MOTIVATION Spinal needle injections are technically demanding procedures. The use of ultrasound image guidance without prior CT and MR imagery promises to improve the efficacy and safety of these procedures in an affordable manner. METHODOLOGY We propose to create a statistical shape model of the lumbar spine and warp this atlas to patient-specific ultrasound images during the needle placement procedure. From CT image volumes of 35 patients, statistical shape model of the L3 vertebra is built, including mean shape and main modes of variation. This shape model is registered to the ultrasound data by simultaneously optimizing the parameters of the model and its relative pose. Ground-truth data was established by printing 3D anatomical models of 3 patients using a rapid prototyping. CT and ultrasound data of these models were registered using fiducial markers. RESULTS Pairwise registration of the statistical shape model and 3D ultrasound images led to a mean target registration error of 3.4 mm, while 81% of all cases yielded clinically acceptable accuracy below the 3.5 mm threshold.

Biomechanically Constrained Groupwise US to CT Registration of the Lumbar Spine

Registration of intraoperative ultrasound (US) with preoperative computed tomography (CT) data for interventional guidance is a subject of immense interest, particularly for percutaneous spinal injections. We propose a biomechanically constrained group-wise registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm simulates an US image from the CT data at each iteration of the registration. This simulated US image is used to calculate an intensity based similarity metric with the real US image. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. Validation is performed on CT and US images from a phantom designed to preserve realistic curvatures of the spine. The technique is able to register initial misalignments of up to 20 mm with a success rate of 82%, and those of up to 10 mm with a success rate of 98.6%.

Fast 3d spine reconstruction of postoperative patients using a multilevel statistical model

Severe cases of spinal deformities such as scoliosis are usually treated by a surgery where instrumentation (hooks, screws and rods) is installed to the spine to correct deformities. Even if the purpose is to obtain a normal spine curve, the result is often straighter than normal. In this paper, we propose a fast statistical reconstruction algorithm based on a general model which can deal with such instrumented spines. To this end, we present the concept of multilevel statistical model where the data are decomposed into a within-group and a between-group component. The reconstruction procedure is formulated as a second-order cone program which can be solved very fast (few tenths of a second). Reconstruction errors were evaluated on real patient data and results showed that multilevel modeling allows better 3D reconstruction than classical models.

Fast 3D Reconstruction of the Spine Using User-Defined Splines and a Statistical Articulated Model

This paper proposes a method for rapidly reconstructing 3D models of the spine from two planar radiographs. For performing 3D reconstructions, users only have to identify on each radiograph a spline that represents the spine midline. Then, a statistical articulated model of the spine is deformed until it best fits these splines. The articulated model used on this method not only models vertebrae geometry, but their relative location and orientation as well. The method was tested on 14 radiographic exams of patients for which reconstructions of the spine using a manual identification method where available. Using simulated splines, errors of 2.2±1.3mm were obtained on endplates location, and 4.1±2.1mm on pedicles. Reconstructions by non-expert users show average reconstruction times of 1.5min, and mean errors of 3.4mm for endplates and 4.8mm for pedicles. These results suggest that the proposed method can be used to reconstruct the human spine in 3D when user interactions have to be minimised.

Multilevel statistical shape models: A new framework for modeling hierarchical structures

Statistical shape models are commonly used in various applications of computer vision. Nevertheless, these models are not well adapted to hierarchical structures. This paper proposes a solution to this problem by presenting a general framework to build multilevel statistical shape models. Based on multilevel component analysis, the idea is to decompose the data into a within-individual and a between-individual component. As a result, several sub-models are deduced and can be treated separately, each level characterizing one sub-model. In this paper, we present a multilevel model of the human spine. The results show that such a modelization offers more flexibility and allows deformations that classical statistical models can simply not generate.

Interactive 3D reconstruction of the spine from radiographs using a statistical shape model and second-order cone programming

Three-dimensional models of the spine are commonly used to diagnose, to treat, and to study spinal deformities. Creating these models is however time-consuming and, therefore, expensive. We propose in this paper a reconstruction method that finds the most likely 3D reconstruction given a maximal error bound on a limited set of landmark locations supplied by the user. This problem can be solved using second-order cone programming, leading to a globally convergent method that is considerably faster than currently available methods. A user can, with our current implementation, interactively modify the landmark locations and receive instantaneous feedback on the effect of those changes on the 3D reconstruction instead of blindly selecting landmarks. The proposed method was validated on a set of 53 patients who had adolescent idiopathic scoliosis using real and synthetic tests. Test results showed that the proposed method is considerably faster than currents methods (about forty times faster), is extremely flexible, and offers comparable accuracy.