A patient specific computational biomechanical model for the entire lumbosacral spinal unit with imposed spondylolysis

Biomechanical evaluation of lumbar spondylolysis repair with various fixation options: A finite element analysis

Objective: This study was designed to compare the biomechanical properties of lumbar spondylolysis repairs using different fixation methods by using three-dimensional finite element analysis.

Methods: Five finite element models (A, B, C, D, and E) of L4-S1 vertebral body were reconstructed by CT images of a male patient (A: intact model; B: spondylolysis model; C: spondylolysis model with intrasegmental direct fixation by Buck screw; D: spondylolysis model with intersegmental indirect fixation by pedicle screw system; E: spondylolysis model with hybrid internal fixation). L5-S1 level was defined as the operative level. After the intact model was verified, six physiological motion states were simulated by applying 500 N concentrated force and 10 Nm torque on the upper surface of L4. The biomechanical properties of the three different internal fixation methods were evaluated by comparing the range of motion (ROM), maximum stress, and maximum displacement.

Results: Compared with Model B, the ROM and maximum displacement of Model C, D, and E decreased. The maximum stress on L5/S1 disc in models A, B, and C was much higher than that in Model D and E under extension and lateral bending conditions. Under axial rotation and lateral bending conditions, the maximum stress of interarticular muscle and internal fixation system in Model B and Model C was significantly higher than that in Model D and Model E. In contrast to Model D, the stress in Model E was distributed in two internal fixation systems.

Conclusion: In several mechanical comparisons, hybrid fixation had better biomechanical properties than other fixation methods. The experimental results show that hybrid fixation can stabilize the isthmus and reduce intervertebral disc stress, which making it the preferred treatment for lumbar spondylolysis.

Biomechanical comparison of four types of instrumentation constructs for revision surgery in lumbar adjacent segment disease: A finite element study

BACKGROUND

Different constructs are applied in revision surgery (RS) for adjacent segment disease (ASD) aiming to further decompress and fixate the affected segment(s) in two ways: replacing or preserving the primary implants. This study aimed to compare the biomechanical properties of four constructs with different configurations.

METHODS

An T12-L5 finite element (FE) model was constructed and validated. Primary surgery was performed at L4-L5 and instrumented from L3 to L5. Thereafter, RS was undertook by decompressing L2-L3 and fixated with implant-replacing construct A, or implant-preserving construct B, C or D. Range of motion (ROM) and intervertebral disc pressure (IDP) were compared. Maximum von Mises stress on the rods between Construct A and B was evaluated.

RESULTS

An obvious reduction of ROM was observed when the FE model was instrumented with four constructs respectively. The overall changing characteristics of ROM were approximately identical among four constructs. The changing characteristic of IDP among four constructs was similar. The degree of IDP reduction of Construct B was comparable to Construct A, while that of Construct C was comparable to Construct D. Maximum von Mises stress on the rods between Construct A and B indicated that no stress concentration was recorded at the locking part of the connector rod.

CONCLUSIONS

The biomechanics of implant-preserving constructs were comparable to the traditional implant-replacing construct. The location of side-by-side connector could not affect the stability of Construct C and D. Construct B might be an optimal choice in RS for less dissection, less complication and more convenience in manipulation.

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs

State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (N = 150). Automatic generation of finite element models to simulate the range of motion (ROM) was successfully performed for five healthy and five pathological FSUs. The results of the simulations were evaluated against the literature and showed comparable ROMs in both healthy and pathological cases, including the alteration of ROM typically observed in severely degenerated FSUs. The major intent of this work is to automate the creation of anatomically accurate patient-specific models by a single pipeline allowing functional modeling of spinal motion in healthy and pathological FSUs. Our approach reduces manual efforts to a minimum and the execution of the entire pipeline including simulations takes approximately 2 h. The automation, time-efficiency and robustness level of the pipeline represents a first step toward its clinical integration.