Personalized X-ray 3-D reconstruction of the scoliotic spine from hybrid statistical and image-based models

A Review of 3D Modalities Used for the Diagnosis of Scoliosis

Spine radiographs in the standing position are the recommended standard for diagnosing idiopathic scoliosis. Though the deformity exists in 3D, its diagnosis is currently carried out with the help of 2D radiographs due to the unavailability of an efficient, low-cost 3D alternative. Computed tomography (CT) and magnetic resonance imaging (MRI) are not suitable in this case, as they are obtained in the supine position. Research on 3D modelling of scoliotic spine began with multiplanar radiographs and later moved on to biplanar radiographs and finally a single radiograph. Nonetheless, modern advances in diagnostic imaging have the potential to preserve image quality and decrease radiation exposure. They include the DIERS formetric scanner system, the EOS imaging system, and ultrasonography. This review article briefly explains the technology behind each of these methods. They are compared with the standard imaging techniques. The DIERS system and ultrasonography are radiation free but have limitations with respect to the quality of the 3D model obtained. There is a need for 3D imaging technology with less or zero radiation exposure and that can produce a quality 3D model for diseases like adolescent idiopathic scoliosis. Accurate 3D models are crucial in clinical practice for diagnosis, planning surgery, patient follow-up examinations, biomechanical applications, and computer-assisted surgery.

Three-dimensional reconstruction of In Vivo human lumbar spine from biplanar radiographs

We present a method for three dimensional (3D) reconstruction of in vivo human lumbar spine from biplanar radiographs with comparable results to Computerised Tomography (CT) scans or Magnetic Resonance Imaging (MRI) models. In this work, we used uncalibrated radiographs to reconstruct the 3D vertebrae and a priori information stored in an Active Shape Model (ASM) that is constructed using the Spherical Demons Algorithm. The method is semi-automatic as bounding boxes are required to delimit the positions of the vertebrae on biplanar radiographs of a patient. Optimisation is based on comparisons between simulated and actual radiographs. Finally, we compare the results to the models generated from MRI and CT scans. The results show the feasibility of generating personalised models of patients from biplanar radiographs.

Reconstruction of three-dimensional lumbar vertebrae from biplanar x-rays

Objective. Vertebrae models from computer tomographic (CT) imaging are extensively used in image-guided surgical systems to deliver percutaneous orthopaedic operations with minimum risks, but patients may be exposed to excess radiation from the pre-operative CT scans. Generating vertebrae models from intra-operative x-rays for image-guided systems can reduce radiation exposure to the patient, and the surgeons can acquire the vertebrae's relative positions during the operation; therefore, we proposed a lumbar vertebrae reconstruction method from biplanar x-rays.Approach. Non-stereo-corresponding vertebral landmarks on x-rays were identified as targets for deforming a set of template vertebrae; the deformation was formulated as a minimisation problem, and was solved using the augmented Lagrangian method. Mean surface errors between the models reconstructed using the proposed method and CT scans were measured to evaluate the reconstruction accuracy.Main results. The evaluation yielded mean errors of 1.27 mm and 1.50 mm inin vitroexperiments on normal vertebrae and pathological vertebrae, respectively; the outcomes were comparable to other template-based methods.Significance. The proposed method is a viable alternative to provide digital lumbar to be used in image-guided systems, where the models can be used as a visual reference in surgical planning and image-guided applications in operations where the reconstruction error is within the allowable surgical error.

Three Dimensional Reconstruction Models for Medical Modalities: A Comprehensive Investigation and Analysis

BACKGROUND

Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field.

DISCUSSION

This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed.

CONCLUSION

The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.

Towards a new 3D classification for adolescent idiopathic scoliosis

STUDY DESIGN

Retrospective analysis of consecutive cases.

OBJECTIVES

To identify clinically relevant three-dimensional (3D) sub-groups for adolescent idiopathic scoliosis (AIS). Classifications for AIS are developed to assist surgeons in surgical planning and therapeutic management. However, current systems are based on two-dimensional (2D) parameters that do not completely describe the 3D deformity. Hence, variations in surgical results based on pre-operative 2D classifications may be attributed to the lack of 3D description.

METHODS

Subjects from a multicenter database of AIS patients were included in this study. All patients had bi-planar radiographs and 3D reconstruction of the entire spine. A clustering algorithm based on fuzzy c-means was utilized to identify sub-groups based on the following ten parameters measured on 3D reconstructions of the spine: Cobb angle, orientation of the plane of maximum curvature of the proximal thoracic, mid-thoracic (MT) and thoracolumbar (TLL) levels, axial rotation of the apical vertebra of the MT and TLL segments, T4-T12 thoracic kyphosis, and L1-S1 lumbar lordosis. Da Vinci views were also generated and analyzed for each patient in the study. A panel of four experienced spine surgeons from the SRS 3D Scoliosis Committee reviewed and evaluated each group to determine if cluster groups were clinically distinct from each other.

RESULTS

The clustering algorithm was able to detect 11 sub-groups. The population size for each cluster varied from 11 to 290. Statistically significant differences were seen between the parameters for each group. Four spine surgeons reviewed the three most representative cases of each group and unanimously agreed that each cluster group represents a sub-group that was not defined in current classifications.

CONCLUSIONS

This study presents a new method of classifying AIS based on a fuzzy clustering algorithm using parameters describing the 3D characteristics of the deformity. Further clinical validation is needed to confirm the usefulness of this classification system.

LEVEL OF EVIDENCE

IV.

Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting

To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast, and automated 3D spine reconstruction method through which a realistic statistical shape model of the spine is fitted to images using convolutional neural networks (CNN). The CNNs automatically detect the anatomical landmarks controlling the spine model deformation through a hierarchical and gradual iterative process. The performance assessment used a set of 68 biplanar radiographs, composed of both asymptomatic subjects and adolescent idiopathic scoliosis patients, in order to compare automated reconstructions with ground truths build using multiple experts-supervised reconstructions. The mean (SD) errors of landmark locations (3D Euclidean distances) were 1.6 (1.3) mm, 1.8 (1.3) mm, and 2.3 (1.4) mm for the vertebral body center, endplate centers, and pedicle centers, respectively. The clinical parameters extracted from the automated 3D reconstruction (reconstruction time is less than one minute) presented an absolute mean error between 2.8° and 4.7° for the main spinal parameters and between 1° and 2.1° for pelvic parameters. Automated and expert's agreement analysis reported that, on average, 89% of automated measurements were inside the expert's confidence intervals. The proposed automated 3D spine reconstruction method provides an important step that should help the dissemination and adoption of 3D measurements in clinical routine.

Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis

PURPOSE

To design a quasi-automated three-dimensional reconstruction method of the spine from biplanar X-rays as the daily used method in clinical routine is based on manual adjustments of a trained operator and the reconstruction time is more than 10 min per patient.

METHODS

The proposed method of 3D reconstruction of the spine (C3-L5) relies first on a new manual input strategy designed to fit clinicians' skills. Then, a parametric model of the spine is computed using statistical inferences, image analysis techniques and fast manual rigid registration.

RESULTS

An agreement study with the clinically used method on a cohort of 57 adolescent scoliotic subjects has shown that both methods have similar performance on vertebral body position and axial rotation (null bias in both cases and standard deviation of signed differences of 1 mm and 3.5° around, respectively). In average, the solution could be computed in less than 5 min of operator time, even for severe scoliosis.

CONCLUSION

The proposed method allows fast and accurate 3D reconstruction of the spine for wide clinical applications and represents a significant step towards full automatization of 3D reconstruction of the spine. Moreover, it is to the best of our knowledge the first method including also the cervical spine. These slides can be retrieved under electronic supplementary material.

Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework

Patient-specific three-dimensional (3-D) bone models are useful for a number of clinical applications such as surgery planning, postoperative evaluation, as well as implant and prosthesis design. Two-dimensional-to-3-D (2-D/3-D) reconstruction, also known as model-to-modality or atlas-based 2-D/3-D registration, provides a means of obtaining a 3-D model of a patient's bones from their 2-D radiographs when 3-D imaging modalities are not available. The preferred approach for estimating both shape and density information (that would be present in a patient's computed tomography data) for 2-D/3-D reconstruction makes use of digitally reconstructed radiographs and deformable models in an iterative, non-rigid, intensity-based approach. Based on a large number of state-of-the-art 2-D/3-D bone reconstruction methods, a unified mathematical formulation of the problem is proposed in a common conceptual framework, using unambiguous terminology. In addition, shortcomings, recent adaptations, and persisting challenges are discussed along with insights for future research.

Evaluating Single-Cell DNA Damage Induced by Enhanced Radiation on a Gold Nanofilm Patch

Although radiotherapy is a general oncology treatment and is often synergistically applied with surgery and chemotherapy, it can cause side effects during and after treatment. Gold nanoparticles were studied as a potential material to enhance radiation to induce damage in cancer cells. However, few studies have been conducted to examine the effects of gold nanofilm on cell impairment under X-ray treatment. This paper describes a microfabrication-based single-cell array platform to evaluate DNA damage induced by enhanced X-ray radiation on gold nanofilm patches (GNFPs). Cancer cells were patterned on GNFPs of different diameters and thicknesses, where each cell was attached on one GNFP. The end-point DNA damage induced by X-ray was examined in situ at the single-cell level using a halo assay. The preliminary data demonstrated that the enhancement of DNA damage was significantly related to the area and thickness of the GNFP. This platform may be hopefully used to establish the mathematical relationships among DNA damage, X-ray dosage, and thickness and area of the GNFP, and further contribute to radiation dosage screening for personalized radiotherapy.

Geometric Torsion in Adolescent Idiopathic Scoliosis: A New Method to Differentiate Between Lenke 1 Subtypes

STUDY DESIGN

Consecutive case series.

OBJECTIVE

To study geometric torsion in thoracic adolescent idiopathic scoliosis (AIS) to propose it as a numerical three-dimensional (3D) parameter that quantifies the scoliosis deformity.

SUMMARY OF BACKGROUND DATA

AIS is a 3D deformity of the spine. The most widely accepted and used classification systems, however, still rely on two-dimensional aspects of x-rays. Yet, a 3D classification of AIS remains elusive because there is no widely accepted 3D parameter in the clinical practice.

METHODS

Analysis of 141 patients with Lenke type-1 deformity recruited in our institution. The Lenke classification was identified by two observers and 3D reconstructions were obtained using biplanar radiographs. Geometric torsion measuring the twisting effect of the spine was computed using a novel technique by approximating local arc lengths at the neutral vertebra in the thoracolumbar segment. An inter- and intragroup statistical analysis was performed to evaluate the torsion index, and how it relates to other 3D indices.

RESULTS

A statistically significant increase in torsion was observed between Lenke 1A (1.15 mm) and Lenke 1C (2.10 mm) subgroups. No differences were found between the Lenke 1B (1.75 mm) subgroup with either of the other two subgroups. An automatic classification based on torsion indices identified two groups: one with high torsion values (3.02 mm) and one with low torsion values (0.82 mm). Statistically significant differences were found between the main thoracic planes of maximum curvature (PMC) orientation of the high-torsion group (73.72°) and the low-torsion group (79.85°). Statistically significant differences were also found for the thoracolumbar/lumbar PMC orientation between the high-torsion group (56.41°) and the low-torsion group (49.25°).

CONCLUSION

These results suggest that a numerical method of describing scoliosis in 3D is within reach. They also suggest the existence of two subgroups of 3D deformations based on torsion values (high and low) with links to PMC orientation.

LEVEL OF EVIDENCE

4.

Semiautomated 3D Spine Reconstruction from Biplanar Radiographic Images: Prediction of Intervertebral Loading in Scoliotic Subjects

The present study proposes a semiautomatic software approach to reconstruct 3D subject-specific musculoskeletal model of thoracolumbar spine from radiographic digitized images acquired with EOS system. The approach is applied to evaluate the intervertebral loads in 38 standing adolescents with mild idiopathic scoliosis. For each vertebra, a set of landmarks was manually identified on radiographic images. The landmark coordinates were processed to calculate the following vertebral geometrical properties in the 3D space (i) location (ii) dimensions; and (iii) rotations. Spherical joints simulated disks, ligaments, and facet joints. Body weight distribution, muscles forces, and insertion points were placed according to physiological-anatomical values. Inverse static analysis, calculating joints' reactions in maintaining assigned spine configuration, was performed with AnyBody software. Reaction forces were computed to quantify intervertebral loads, and correlation with the patient anatomical parameters was then checked. Preliminary validation was performed comparing the model outcomes with that obtained from other authors in previous modeling works and from in vivo measurements. The comparison with previous modeling works and in vivo studies partially fulfilled the preliminary validation purpose. However, minor incongruities were pointed out that need further investigations. The subjects' intervertebral loads were found significantly correlated with the anatomical parameters in the sagittal and axial planes. Despite preliminary encouraging results that support model suitability, future investigations to consolidate the proposed approach are necessary. Nonetheless, the present method appears to be a promising tool that once fully validated could allow the subject-specific non-invasive evaluation of a deformed spine, providing supplementary information to the routine clinical examination and surgical intervention planning.

Geometric Torsion in Adolescent Idiopathic Scoliosis: A Surgical Outcomes Study of Lenke Type 1 Patients

STUDY DESIGN

Consecutive case series analysis.

OBJECTIVE

To evaluate the surgical outcomes of patients with thoracic adolescent idiopathic scoliosis (AIS) in relation to different degrees of geometric torsion.

SUMMARY OF BACKGROUND DATA

AIS is a three-dimensional (3D) deformity of the spine. A 3D classification of AIS, however, remains elusive because there is no widely accepted 3D parameter in the clinical practice. Recently, a new method of estimating geometric torsion has been proposed and detected two potential new 3D subgroups based on geometric torsion values.

METHODS

This is an analysis of 93 patients with Lenke type-1 deformity from our institution. 3D reconstructions were obtained using biplanar radiographs both pre- and postoperatively. Geometric torsion was computed using a novel technique by approximating local arc lengths at the neutral vertebra in the thoracolumbar segment. An inter- and intragroup statistical analysis was performed to compare clinical indices of patients with different torsion values. A qualitative assessment was also performed on each patient by two senior staff surgeons.

RESULTS

Statistically significant differences were observed in clinical indices between high (2.85 mm) and low torsion (0.83 mm) Lenke type 1 subgroups. Preoperatively, the high torsion group showed higher Cobb angle values in the thoracic segment (71.18° vs. 63.74°), as well as higher angulation in the thoracolumbar plane of maximum deformity (67.79° vs. 53.30°). Postoperatively, a statistically significant difference was found in the orientation of the plane of maximum deformity in the thoracolumbar segment between the high and low torsion groups (47.95° vs. 30.03°). Results from the qualitative evaluation of surgical results showed different results between the two staff surgeons.

CONCLUSION

These results suggest a link between preoperative torsion values and surgical outcomes within Lenke type 1 deformities. These results will need to be validated by an independent group, as it is a single-center study.

LEVEL OF EVIDENCE

4.

Postoperative 3D spine reconstruction by navigating partitioning manifolds

PURPOSE

The postoperative evaluation of scoliosis patients undergoing corrective treatment is an important task to assess the strategy of the spinal surgery. Using accurate 3D geometric models of the patient's spine is essential to measure longitudinal changes in the patient's anatomy. On the other hand, reconstructing the spine in 3D from postoperative radiographs is a challenging problem due to the presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine.

METHODS

This paper describes the reconstruction problem by searching for the optimal model within a manifold space of articulated spines learned from a training dataset of pathological cases who underwent surgery. The manifold structure is implemented based on a multilevel manifold ensemble to structure the data, incorporating connections between nodes within a single manifold, in addition to connections between different multilevel manifolds, representing subregions with similar characteristics.

RESULTS

The reconstruction pipeline was evaluated on x-ray datasets from both preoperative patients and patients with spinal surgery. By comparing the method to ground-truth models, a 3D reconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30 postoperative scoliotic patients, while handling patients with highly deformed spines.

CONCLUSIONS

This paper illustrates how this manifold model can accurately identify similar spine models by navigating in the low-dimensional space, as well as computing nonlinear charts within local neighborhoods of the embedded space during the testing phase. This technique allows postoperative follow-ups of spinal surgery using personalized 3D spine models and assess surgical strategies for spinal deformities.

Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models

PURPOSE

The classification of three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. Recent studies have investigated pattern classification based on explicit clinical parameters. An emerging trend however seeks to simplify complex spine geometries and capture the predominant modes of variability of the deformation. The objective of this study is to perform a 3D characterization and morphology analysis of the thoracic and thoraco/lumbar scoliotic spines (cross-sectional study). The presence of subgroups within all Lenke types will be investigated by analyzing a simplified representation of the geometric 3D reconstruction of a patient's spine, and to establish the basis for a new classification approach based on a machine learning algorithm.

METHODS

Three-dimensional reconstructions of coronal and sagittal standing radiographs of 663 patients, for a total of 915 visits, covering all types of deformities in adolescent idiopathic scoliosis (single, double and triple curves) and reviewed by the 3D Classification Committee of the Scoliosis Research Society, were analyzed using a machine learning algorithm based on stacked auto-encoders. The codes produced for each 3D reconstruction would be then grouped together using an unsupervised clustering method. For each identified cluster, Cobb angle and orientation of the plane of maximum curvature in the thoracic and lumbar curves, axial rotation of the apical vertebrae, kyphosis (T4-T12), lordosis (L1-S1) and pelvic incidence were obtained. No assumptions were made regarding grouping tendencies in the data nor were the number of clusters predefined.

RESULTS

Eleven groups were revealed from the 915 visits, wherein the location of the main curve, kyphosis and lordosis were the three major discriminating factors with slight overlap between groups. Two main groups emerge among the eleven different clusters of patients: a first with small thoracic deformities and large lumbar deformities, while the other with large thoracic deformities and small lumbar curvature. The main factor that allowed identifying eleven distinct subgroups within the surgical patients (major curves) from Lenke type-1 to type-6 curves, was the location of the apical vertebra as identified by the planes of maximum curvature obtained in both thoracic and thoraco/lumbar segments. Both hypokyphotic and hyperkypothic clusters were primarily composed of Lenke 1-4 curve type patients, while a hyperlordotic cluster was composed of Lenke 5 and 6 curve type patients.

CONCLUSION

The stacked auto-encoder analysis technique helped to simplify the complex nature of 3D spine models, while preserving the intrinsic properties that are typically measured with explicit parameters derived from the 3D reconstruction.

3D spine reconstruction of postoperative patients from multi-level manifold ensembles

The quantitative assessment of surgical outcomes using personalized anatomical models is an essential task for the treatment of spinal deformities such as adolescent idiopathic scoliosis. However an accurate 3D reconstruction of the spine from postoperative X-ray images remains challenging due to presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine. In this paper, we formulate the reconstruction problem as an optimization over a manifold of articulated spine shapes learned from pathological training data. The manifold itself is represented using a novel data structure, a multi-level manifold ensemble, which contains links between nodes in a single hierarchical structure, as well as links between different hierarchies, representing overlapping partitions. We show that this data structure allows both efficient localization and navigation on the manifold, for on-the-fly building of local nonlinear models (manifold charting). Our reconstruction framework was tested on pre- and postoperative X-ray datasets from patients who underwent spinal surgery. Compared to manual ground-truth, our method achieves a 3D reconstruction accuracy of 2.37 +/- 0.85 mm for postoperative spine models and can deal with severe cases of scoliosis.

Real-scale 3D models of the scoliotic spine from biplanar radiography without calibration objects

This paper presents a new method for modelling the spines of subjects and making accurate 3D measurements using standard radiologic systems without requiring calibration objects. The method makes use of the focal distance and statistical models for estimating the geometrical parameters of the system. A dataset of 32 subjects was used to assess this method. The results show small errors for the main clinical indices, such as an RMS error of 0.49° for the Cobb angle, 0.50° for kyphosis, 0.38° for lordosis, and 2.62mm for the spinal length. This method is the first to achieve this level of accuracy without requiring the use of calibration objects when acquiring radiographs. We conclude that the proposed method allows for the evaluation of scoliosis with a much simpler setup than currently available methods.

Three-dimensional spine model reconstruction using one-class SVM regularization

Statistical shape models have become essential for medical image registration or segmentation and are used in many biomedical applications. These models are often based on Gaussian distributions learned from a training set. We propose in this paper a shape model which does not rely on the estimation of a Gaussian distribution, but on similarities computed with a kernel function. Our model takes advantage of the one-class support vector machine (OCSVM) to do so. In this context, we propose in this paper a method for reconstructing the spine of scoliotic patients using OCSVM regularization. Current state-of-the-art methods use conventional statistical shape models, and the reconstruction is commonly processed by minimizing a Mahalanobis distance. Nevertheless, when a shape differs significantly from the statistical model, the associated Mahalanobis distance often overstates the need for statistical regularization. We show that OCSVM regularization is more robust and is less sensitive to weak landmarks definition and is hardly influenced by the presence of outliers in the training data. The proposed OCSVM model applied to 3-D spine reconstruction was evaluated on real patient data, and results showed that our approach allows precise reconstruction.

3D analysis of congenital scoliosis due to hemivertebra using biplanar radiography

INTRODUCTION

This study aims to investigate the use of biplanar radiography for assessing congenital scoliosis due to hemivertebra in 3D.

MATERIALS AND METHODS

A reconstruction method was developed to model 3D spines with congenital scoliosis from biplanar radiography. 3D measurements quantifying the global posture, scoliotic deformities and imbalance and describing the shape and pose of the hemivertebra were automatically computed. Five cases of congenital scoliosis were analyzed and the accuracy of the method was evaluated by comparing 3D reconstructions from biplanar radiography with 3D segmentations generated from CT.

RESULTS

The mean shape accuracy was 1.8 mm (1.5 mm for the vertebral bodies and pedicles and 2.2 mm for the posterior arches).

CONCLUSION

Biplanar radiography can be considered an interesting tool for clinical follow-up of congenital scoliosis as it overcomes some limitations of the analyses based on CT or anteroposterior X-ray: head to feet acquisition, low radiation dose and provides a set of automatically computed postural and morphological parameters in 3D.

Classification of three-dimensional thoracic deformities in adolescent idiopathic scoliosis from a multivariate analysis

PURPOSE

Understanding how to classify and quantify three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. The objective of this study was to perform a 3D manifold characterization of scoliotic spines demonstrating thoracic deformations using a novel geometric and intuitive statistical tool to determine patterns in pathological cases.

METHODS

Personalized 3D reconstructions of thoracic (T)/lumbar (L) spines from a cohort of 170 Lenke Type-1 patients were analyzed with a non-linear manifold embedding algorithm in order to reduce the high-dimensionality of the data, using statistical properties of neighbouring spine models. We extracted sub-groups of the data from the underlying manifold structure using an unsupervised clustering algorithm to understand the inherent distribution and determine classes of pathologies which appear from the low-dimensional space.

RESULTS

For Lenke Type-1 patients, four clusters were detected from the low-dimensional manifold of 3D models: (1) normal kyphosis (T) with hyper-lordosis (L) and high Cobb angles (37 cases), (2) low kyphosis (T) and normal lordosis (L), with high rotation of plane of maximum curvature (55 cases), (3) hypo-kyphotic (T) and hyper-lordosis (L) (21 cases) and (4) hyper-kyphotic (T) with strong vertebral rotation (57 cases). Results show the manifold representation can potentially be useful for classification of 3D spinal pathologies such as idiopathic scoliosis and serve as a tool for understanding the progression of deformities in longitudinal studies.

CONCLUSIONS

Quantitative evaluation illustrates that the complex space of spine variability can be modeled by a low-dimensional manifold and shows the existence of an additional hyper-kyphotic subgroup from the cohort of 3D spine reconstructions of Lenke Type-1 patients when compared with previous findings on the 3D classification of spinal deformities.

Automatic inference of articulated spine models in CT images using high-order Markov Random Fields

In this paper, we introduce a novel and efficient approach for inferring articulated 3D spine models from operative images. The problem is formulated as a Markov Random Field which has the ability to encode global structural dependencies to align CT volume images. A personalized geometrical model is first reconstructed from preoperative images before surgery, and subsequently decomposed as a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is achieved by optimizing the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher-order cliques (groups of vertebrae) are introduced to integrate consistency in regional curves. Local vertebra modifications are achieved through a constrained mesh relaxation technique. Optimization of model parameters in a multimodal context is achieved using efficient linear programming and duality. Experimental and clinical evaluation of the vertebra model alignment obtained from the proposed method gave promising results. Quantitative comparison to expert identification yields an accuracy of 1.8±0.7mm based on the localization of surgical landmarks.

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.