Recent Advances in Coupled MBS and FEM Models of the Spine—A Review

CT-based surrogate parameters for MRI-based disc height and endplate degeneration in the lumbar spine

PURPOSE

This study investigated potential use of computed tomography (CT)-based parameters in the lumbar spine as a surrogate for magnetic resonance imaging (MRI)-based findings.

METHODS

In this retrospective study, all individuals, who had a lumbar spine CT scan and MRI between 2006 and 2012 were reviewed (n = 198). Disc height (DH) and endplate degeneration (ED) were evaluated between Th12/L1-L5/S1. Statistics consisted of Spearman correlation and univariate/multivariable regression (adjusting for age and gender).

RESULTS

The mean CT-DH increased kranio-caudally (8.04 millimeters (mm) at T12/L1, 9.17 mm at L1/2, 10.59 mm at L2/3, 11.34 mm at L3/4, 11.42 mm at L4/5 and 10.47 mm at L5/S1). MRI-ED was observed in 58 (29%) individuals. CT-DH and MRI-DH had strong to very strong correlations (rho 0.781-0.904, p < .001). MRI-DH showed higher absolute values than CT-DH (mean of 1.76 mm). There was a significant association between CT-DH and MRI-ED at L2/3 (p = .006), L3/4 (p = .002), L4/5 (p < .001) and L5/S1 (p < .001). A calculated cut-off point was set at 11 mm.

CONCLUSIONS

In the lumbar spine, there is a correlation between disc height on CT and MRI. This can be useful in trauma and emergency cases, where CT is readily available in the lack of an MRI. In addition, in the middle and lower part of the lumbar spine, loss of disc height on CT scans is associated with more pronounced endplate degeneration on MRIs. If the disc height on CT scans is lower than 11 mm, endplate degeneration on MRIs is likely more pronounced.

LEVEL AND DESIGN

Level III, a retrospective study.

Musculoskeletal spine modeling in large patient cohorts: how morphological individualization affects lumbar load estimation

Introduction: Achieving an adequate level of detail is a crucial part of any modeling process. Thus, oversimplification of complex systems can lead to overestimation, underestimation, and general bias of effects, while elaborate models run the risk of losing validity due to the uncontrolled interaction of multiple influencing factors and error propagation. Methods: We used a validated pipeline for the automated generation of multi-body models of the trunk to create 279 models based on CT data from 93 patients to investigate how different degrees of individualization affect the observed effects of different morphological characteristics on lumbar loads. Specifically, individual parameters related to spinal morphology (thoracic kyphosis (TK), lumbar lordosis (LL), and torso height (TH)), as well as torso weight (TW) and distribution, were fully or partly considered in the respective models according to their degree of individualization, and the effect strengths of these parameters on spinal loading were compared between semi- and highly individualized models. T-distributed stochastic neighbor embedding (T-SNE) analysis was performed for overarching pattern recognition and multiple regression analyses to evaluate changes in occurring effects and significance. Results: We were able to identify significant effects (p < 0.05) of various morphological parameters on lumbar loads in models with different degrees of individualization. Torso weight and lumbar lordosis showed the strongest effects on compression (β ≈ 0.9) and anterior-posterior shear forces (β ≈ 0.7), respectively. We could further show that the effect strength of individual parameters tended to decrease if more individual characteristics were included in the models. Discussion: The induced variability due to model individualization could only partly be explained by simple morphological parameters. Our study shows that model simplification can lead to an emphasis on individual effects, which needs to be critically assessed with regard to in vivo complexity. At the same time, we demonstrated that individualized models representing a population-based cohort are still able to identify relevant influences on spinal loading while considering a variety of influencing factors and their interactions.

Biomechanical assessment of mandibular fracture fixation using finite element analysis validated by polymeric mandible mechanical testing

The clinical finite element analysis (FEA) application in maxillofacial surgery for mandibular fracture is limited due to the lack of a validated FEA model. Therefore, this study aims to develop a validated FEA model for mandibular fracture treatment, by assessing non-comminuted mandibular fracture fixation. FEA models were created for mandibles with single simple symphysis, parasymphysis, and angle fractures; fixated with 2.0 mm 4-hole titanium miniplates located at three different configurations with clinically known differences in stability, namely: superior border, inferior border, and two plate combinations. The FEA models were validated with series of Synbone polymeric mandible mechanical testing (PMMT) using a mechanical test bench with an identical test set-up. The first outcome was that the current understanding of stable simple mandibular fracture fixation was reproducible in both the FEA and PMMT. Optimal fracture stability was achieved with the two plate combination, followed by superior border, and then inferior border plating. Second, the FEA and the PMMT findings were consistent and comparable (a total displacement difference of 1.13 mm). In conclusion, the FEA and the PMMT outcomes were similar, and hence suitable for simple mandibular fracture treatment analyses. The FEA model can possibly be applied for non-routine complex mandibular fracture management.

Muscle-driven forward dynamic active hybrid model of the lumbosacral spine: combined FEM and multibody simulation

Most spine models belong to either the musculoskeletal multibody (MB) or finite element (FE) method. Recently, coupling of MB and FE models has increasingly been used to combine advantages of both methods. Active hybrid FE-MB models, still rarely used in spine research, avoid the interface and convergence problems associated with model coupling. They provide the inherent ability to account for the full interplay of passive and active mechanisms for spinal stability. In this paper, we developed and validated a novel muscle-driven forward dynamic active hybrid FE-MB model of the lumbosacral spine (LSS) in ArtiSynth to simultaneously calculate muscle activation patterns, vertebral movements, and internal mechanical loads. The model consisted of the rigid vertebrae L1-S1 interconnected with hyperelastic fiber-reinforced FE intervertebral discs, ligaments, facet joints, and force actuators representing the muscles. Morphological muscle data were implemented via a semi-automated registration procedure. Four auxiliary bodies were utilized to describe non-linear muscle paths by wrapping and attaching the anterior abdominal muscles. This included an abdominal plate whose kinematics was optimized using motion capture data from upper body movements. Intra-abdominal pressure was calculated from the forces of the abdominal muscles compressing the abdominal cavity. For the muscle-driven approach, forward dynamics assisted data tracking was used to predict muscle activation patterns that generate spinal postures and balance the spine without prescribing accurate spinal kinematics. During calibration, the maximum specific muscle tension and spinal rhythms resulting from the model dynamics were evaluated. To validate the model, load cases were simulated from -10° extension to +30° flexion with weights up to 20 kg in both hands. The biomechanical model responses were compared with in vivo literature data of intradiscal pressures, intra-abdominal pressures, and muscle activities. The results demonstrated high agreement with this data and highlight the advantages of active hybrid modeling for the LSS. Overall, this new self-contained tool provides a robust and efficient estimation of LSS biomechanical responses under in vivo similar loads, for example, to improve pain treatment by spinal stabilization therapies.

Advances in Biomechanics-Based Motion Analysis

Motion patterns in humans have been closely associated with neurological/musculoskeletal/behavioral/psychological health issues and competitive sports performance [...].