A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

PURPOSE

To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

METHODS

We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC.

RESULTS

Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001).

CONCLUSION

A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

Introduction

Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Methods

Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated.

Results

Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001).

Conclusion

Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Effects of laminectomy and facetectomy on the stability of the lumbar motion segment

A ligamentous, nonlinear, sliding contact, three-dimensional finite element (FE) model of L2-L3 complex was developed to investigate the biomechanical effect of laminectomy with and without facetectomy. The L2-L3 FE model was validated against experimental study under various physiological loadings and found to match well with the experimental data. Four iatrogenic models (unilateral laminectomy, unilateral laminectomy with unilateral facetectomy, unilateral laminectomy with bilateral facetectomy and total bilateral laminectomy) were evaluated under flexion, extension, torsion, lateral bending, anterior and posterior shear load vectors to determine alterations in kinematics and annulus stress. Results show that total laminectomy with facetectomy induces considerable increase in motion and annulus stress, except for lateral bending, whereas unilateral laminectomy shows the least increases.

Poroelastic Analysis of Lumbar Spinal Stability in Combined Compression and Anterior Shear

OBJECTIVE

A three-dimensional poroelastic finite element (FE) L2-L3 model was developed to study lumbar spinal instability and intrinsic parameters in the intervertebral disc (IVD).

METHODS

The FE model took into consideration poroelasticity of the IVD and viscoelasticity of the annulus fibers and ligaments to predict the time-dependent behavior. To simulate a holding task, the motion segment was subjected to a combined loading of constant compressive load (1600 N) and anterior shear (200 N) for 2 hours, and the role of facet joints and ligaments in the biomechanical response was investigated by removal of unilateral/bilateral facets, posterior ligaments (supraspinous and interspinous), and facets and ligaments.

RESULTS

The results show the stabilizing role of the facets and ligaments in resisting anterior shear and sagittal rotation under combined loading over time. The main pathway of fluid movement was found to permeate through the central region of the endplate, and the fluid diffusion occurred earlier at the posterior nucleus than the anterior nucleus. The fluid loss from the nucleus dictated the time-dependent motion under the sustained loading, whereas the intrinsic properties of ligaments/annulus fibers played a role only in the early stage of the loading.

CONCLUSION

The predicted results using poroelastic elements provide new insight into the IVD in providing the spinal stiffness under combined loading.

Effect of facetectomy on lumbar spinal stability under sagittal plane loadings

STUDY DESIGN

A study using an anatomically accurate finite-element model of a L2-L3 motion segment to investigate the biomechanical effects of graded bilateral and unilateral facetectomies of L3 under flexion and extension loadings.

OBJECTIVE

To predict the amount of facetectomy on lumbar motion segment that would cause segmental instability, therefore enhancing the understanding concerning the role of the facet under sagittal loadings.

SUMMARY OF BACKGROUND DATA

This study provides a quantitative study on the role of facets in preserving segmental lumbar stability. Previous analytical models lack of three-dimensional structural characterization and insufficient element representation for facet joints.

METHODS

A validated finite-element L2-L3 model was subjected to sagittal loadings at 7.5 Nm. Effects of ligaments and facets were examined to establish their relative importance on segment response. The effect of iatrogenic changes (graded unilateral and bilateral facetectomy) was then investigated under these loadings to predict the alterations in terms of gross external (angular and coupled) responses, flexibilities, and facet load.

RESULTS

This study shows the importance of preserving ligaments to prevent rotational instabilities for motion segment under flexion. The effect of the facetectomy on the motion segment is insignificant under flexion. In extension, unilateral facetectomy and resection on contralateral facet markedly alters the rotational motion and flexibilities as well as coupled motions. Also, unilateral complete facetectomy with resection of less than 100% on contralateral facet generates high facet load.

CONCLUSIONS

Clinically, this study suggests that it may be appropriate to incorporate additional stabilization procedure in restoring the spinal strength and stability for surgical intervention of unilateral complete facetectomy and resection on contralateral facet. The exploitation of the finite-element method to simulate clinically related situations permits an improved understanding of lumbar spinal stability to assist in defining clinical expectation for various forms of surgical intervention of the operative procedures.

Statistical factorial analysis on the material property sensitivity of the mechanical responses of the C4–C6 under compression, anterior and posterior shear

A systematic approach using factorial analysis was conducted on the C4-C6 finite element model to analyse the influence of six spinal components (cortical shell, vertebral body, posterior elements, endplate, disc annulus and disc nucleus) on the internal stresses and external biomechanical responses under compression, anterior and posterior shear. Results indicated that the material properties variation of the disc annulus has a significant influence on both the external biomechanical responses and internal stress of the disc annulus and its neighboring hard bones. The study reveals for the first time, the significant influence of the cancellous bone under compression, while variation in the cortical shell modulus has a high influence under anterior and posterior shear. The study also reveals that the effects of interaction between two main components are insignificant.

Quantitative three-dimensional anatomy of cervical, thoracic and lumbar vertebrae of Chinese Singaporeans

This paper details the quantitative three-dimensional anatomy of cervical, thoracic and lumbar vertebrae (C3-T12) of Chinese Singaporean subjects based on 220 vertebrae from 10 cadavers. The purpose of the study was to measure the linear dimensions, angulations and areas of individual vertebra, and to compare the data with similar studies performed on Caucasian specimens. Measurements were taken with the aid of a three-dimensional digitiser. The means and standard errors for linear, angular and area dimensions of the vertebral body, spinal canal, pedicle, and spinous and transverse processes were obtained for each vertebra. Compared to the Caucasian data, all the dimensions were found to be smaller. Of significance were the spinal canal area, and pedicle width and length, which were smaller by 31.7%, 25.7% and 22.1% on average, respectively. A slight divergence, instead of convergence, was found from T8 to T12. According to the findings, the use of a transpedicle screw may not be feasible. The results can also provide more accurate modelling for analysis and design of spinal implants and instrumentations, and also allow more precise clinical diagnosis and management of the spine in Chinese Singaporeans.

Finite-Element Analysis for Lumbar Interbody Fusion Under Axial Loading

A parametric study was conducted to evaluate axial stiffness of the interbody fusion, compressive stress, and bulging in the endplate due to changes in the spacer position with/without fusion bone using an anatomically accurate and validated L2-L3 finite-element model exercised under physiological axial compression. The results show that the spacer plays an important role in initial stability for fusion, and high compressive force is predicted at the ventral endplate for the models with the spacer and fusion bone together. By varying the positioning of the spacer anteriorly along anteroposterior axis, no significant change in terms of axial stiffness, compressive stress, and bulging of the endplate are predicted for the implant model. The findings suggest that varying the spacer position in surgical situations does not affect the mechanical behavior of the lumbar spine after interbody fusion.

The Biomechanics of Lumbar Graded Facetectomy Under Anterior-Shear Load

In this paper, an anatomically accurate three-dimensional finite-element (FE) model of the human lumbar spine (L2-L3) was used to study the biomechanical effects of graded bilateral and unilateral facetectomies of L3 under anterior shear. The intact L2-L3 FE model was validated under compression, tension, and shear loading and the predicted responses matched well with experimental data. The gross external (translational and coupled) responses, flexibilities, and facet load were delineated for these iatrogenic changes. Results indicted that unilateral facetectomy of greater than 75% and bilateral facetectomy of 75% or more resection markedly alter the translational displacement and flexibilities of the motion segment. This study suggests that fixation or fusion to restore strength and stability of the lumbar spine may be required for surgical intervention of greater than 75% facetectomy.

Quantitative three-dimensional anatomy of lumbar vertebrae in Singaporean Asians

This paper details the quantitative three-dimensional anatomy of lumbar vertebrae L1-L5 from Asian (Singaporean) subjects based on 60 lumbar vertebrae from 12 cadavers. The purpose of the study was to measure the dimensions of the various parameters of the lumbar vertebrae and thereafter to compare the data with a study performed on Caucasian specimens. Measurements were taken with the aid of a three-dimensional digitiser. The means and standard errors for linear, angular and area dimensions of the vertebral body, spinal canal, pedicle, and spinous and transverse processes were obtained for each lumbar vertebra. From this comparison, it was found that the dimensions of the vertebral body of the Asian subjects are slightly larger, with a maximum average difference of 8% for the posterior vertebral body height. The dimensions of the spinal canal, pedicle, and spinous and transverse processes of Asian subjects are smaller. The greatest difference can be found in the spinal canal area and pedicle width, which are smaller by an average of 30% and 20%, respectively. With the exception of the spinal canal depth, spinal canal area and pedicle width, all other parameters compared show a similar trend. The findings can provide more accurate modelling for analysis and spinal implant design and also allow more precise clinical diagnosis in sub-Asian groups.

Biomechanical study of C2 (Axis) fracture: effect of restraint

INTRODUCTION

In human, the cervical spine region is very susceptible to impact injury. The complex structures of C1 and C2 serve to transmit the weight of the cranium to the greatly similar structural cervical spine from C3 caudally. Application of sudden disruption forces will detach the junction of the cervico-cranium with the pars interarticularis of the neural arch of C2 from the lower cervical spine by fracturing, as in a "hangman's fracture". Severe falls or blows to the head from heavy objects will cause the fracture of the odontoid process of C2. Many biomechanical studies were conducted based on full restraint of the inferior aspects of isolated C2 to produce odontoid fracture. In this study, two different restraining conditions of C2 were adopted experimentally to determine the absolute fracture load and the corresponding fracture patterns that are common to C2.

MATERIALS AND METHODS

Nine C2 vertebrae obtained from cadaver spines, ranging from 51 to 80 years, were used. Two specified restraint conditions were employed: (1) fully constrain the posterior element postero-anteriorly up to one-quarter of the inferior facet; and (2) fixing of C2 by a specially-designed rig whereby the body of C2 embedded in the pivoted cup and its inferior facets positioned on top of two lateral plates. Antero-posterior shear force was applied on the anterior articulating facet of the dens until failure.

RESULTS

These specified restraint conditions had resulted in specific fracture of C2. Antero-posterior shear force ranges from 840 to 1220 N was required to cause fracture across the pars interarticularis under restraint condition 1. Failure load of between 900 and 1500 N was found to cause odontoid fracture under restraint condition 2. These values are in agreement with published data.

CONCLUSIONS

The biomechanical response of C2 was specific to the mode of restraint conditions of C2. In reality, depending on the force vector applied to the head, precise posture at the time of trauma, spinal geometry, and physical properties, different types of C2 fracture patterns may happen. These findings are of potentials for the biomechanical correlation and validation study of C2 vertebra using analytical approaches, and in the surgical anterior screw fixation of odontoid fracture.

Experimental investigation of failure load and fracture patterns of C2 (axis)

The injury mechanism and magnitude of failure load of C2 fractures are important in the clinical treatment of its fixation. The magnitudes of the failure load of C2 and the mechanism of injury in vivo are uncertain. Accordingly, nine C2 vertebrae obtained from cadaver spines, ranging in age from 51 to 80 years, were used for the study. Special restraint conditions were applied to yield specific fracture of C2. With the posterior element potted postero-anteriorly up to one-quarter of the inferior facet, posterior shear force ranging from 840 to 1220N was required to cause fracture across the pars interarticularis. For odontoid fracture study, a special rig was fabricated to encapsulate the body of C2 in a cell using ISOPON, and a thin layer of ISOPON sandwiched between the inferior facets and two lateral plates. The assembled rig permits slight sagittal movement of C2 about the cup lateral pivot supports. Failure load of between 900 and 1500N was recorded for odontoid fracture. These values are in agreement with published data. The experiment carried out under these two different restraint conditions had specifically resulted in different fractures of C2. In reality, depending on factors such as the inclination of this force vector applied to the head, the precise posture at the time of trauma, the spinal geometry, and the physical properties, different types of fracture patterns of C2 may be produced. This additional data will be useful in the biomechanical study of C2 vertebra using analytical approaches, and in surgical anterior/posterior fixation using screws.

Nonlinear finite-element analysis of the lower cervical spine (C4-C6) under axial loading

This study was conducted to develop a detailed, nonlinear three-dimensional geometrically and mechanically accurate finite-element model of the human lower cervical spine using a high-definition digitizer. This direct digitizing process also offers an additional method in the development of the finite-element model for the human cervical spine. The biomechanical response of the finite-element model was validated and corresponded closely with the published experimental data and existing finite-element models under axial compressive loading. Furthermore, the results indicated that the cervical spine segment response is nonlinear with increasing stiffness at higher loads. As a logical step, a parametric study was conducted by evaluating the biomechanical response related to the changes in the modeling techniques of the finite-element model and the mechanical properties of the disk annulus. Variations of the predicted horizontal disk bulge were investigated under axial compressive displacements for the normal model, the model without facet articulations, and the model without nucleus. Removal of nucleus fluids causes an inward bulge of the inner annulus layers, with the displacement magnitude dependent on external loads. The result indicates that the nucleus fluid plays an important role in cervical spine mechanics. Simulated facetectomy indicates a decrease in the stiffness of the cervical spine. The study shows that, in reality, the stiffness of the lower cervical spine depends closely on factors such as the spinal geometry and physical properties, thereby resulting in various force and displacement responses.

Evaluation of the role of ligaments, facets and disc nucleus in lower cervical spine under compression and sagittal moments using finite element method

Cervical spinal instability due to ligamentous injury, degenerated disc and facetectomy is a subject of great controversy. There is no analytical investigation reported on the biomechanical response of cervical spine in these respects. Parametric study on the roles of ligaments, facets, and disc nucleus of human lower cervical spine (C4-C6) was conducted for the very first time using noninvasive finite element method.A three-dimensional (3D) finite element (FE) model of the human lower cervical spine, consisted of 11,187 nodes and 7730 elements modeling the bony vertebrae, articulating facets, intervertebral disc, and associated ligaments, was developed and validated against the published data under three load configurations: axial compression; flexion; and extension. The FE model was further modified accordingly to investigate the role of disc, facets and ligaments in preserving cervical spine motion segment stability in these load configurations. The passive FE model predicted the nonlinear force displacement response of the human cervical spine, with increasing stiffness at higher loads. It also predicted that ligaments, facets or disc nucleus are crucial to maintain the cervical spine stability, in terms of sagittal rotational movement or redistribution of load. FE method of analysis is an invaluable application that can supplement experimental research in understanding the clinical biomechanics of the human cervical spine.

First cervical vertebra (atlas) fracture mechanism studies using finite element method

Injury mechanisms and stress distribution patterns are important in the clinical evaluation of spinal injuries. Recognition and interpretation of the failure patterns help to determine spinal instability and consequently the choice of treatment. Although, the biomechanics responses of the atlas have received much attention, it has not been investigated using theoretical modeling. Mathematical techniques such as finite element model will provide further understanding to the injury mechanisms of the atlas, which is important for the prevention, diagnosis, and treatment of spinal injuries. In the present study, a detailed three-dimensional finite element model of the human atlas (C1) was constructed, with the geometrical data obtained using a three-dimensional digitizer. Anterior arch, superior/inferior articular processes, transverse processes, posterior arch and posterior tubercule were modeled using eight-noded brick elements. Using the material properties from literature, the 7808-finite element model was exercised under three simulated axial compressive mode of pressure loading and boundary conditions to investigate the sites of failure reported in vivo and in vitro. This report demonstrates high concentration of localized stress at the anterior and posterior archs of the atlas, which agrees well with those reported in the literature. Furthermore, under simulated hyperextension, our results agreed well with the experimental findings, which show that the groove of the posterior arch is subjected to enormous bending moment. The close agreement of the failure location provided confidence to perform further analysis and in vitro experiments. These results may be potentially used to supplement experimental research in understanding the clinical biomechanics of the atlas.

Analytical static stress analysis of first cervical vertebra (atlas)

INTRODUCTION

Fracture of the atlas was first described by Jefferson (1920). He theorised a bursting mechanism of fracture as the occipital condyles were driven into the atlas. Experimental studies by Hays and Alker (1988) and Panjabi et al (1991) were also conducted to explain the injury mechanisms. Injury mechanisms and fracture patterns are important in the clinical evaluation of spinal injuries. Recognition and interpretation of the fracture patterns help to determine the spinal instability and consequently the choice of treatment. Although the fracture mechanics of the atlas have received much attention, it has not been investigated using theoretical modelling.

MATERIALS AND METHODS

A high-definition digitiser was used to obtain the geometrical data for the finite element mesh generation. Contrary to the widely used method, such as computed tomography scan for geometric extraction, the direct digitising process of the dried specimen reliably preserves the accurate topography of up to 0.1-mm interval of the original structure. The finite element model was exercised under an axial compressive mode of pressure loading to investigate the sites of failure reported in vivo and in vitro.

RESULTS

Using material properties from literature, the predicted results from the 7808-finite element model demonstrate high concentration of localised stress at the anterior and posterior arch of the atlas, which agrees well with those reported in the literature. Furthermore, our results are also in good agreement with the findings reported by Panjabi et al (1991), which show that the groove of the posterior arch is subjected to enormous bending moment under simulated hyperextension conditions.

CONCLUSIONS

The close agreement of the failure location provided confidence to perform further analysis and in vitro experiments. The predicted results from finite element analysis may be potentially used to supplement experimental research in understanding the clinical biomechanics of the C1.