Date of Award
Doctor of Philosophy (PhD)
Dr. Melinda K. Harman
Dr. Hai Yao
Dr. Jeremy J. Mercuri
Dr. Timothy P. McHenry
This study developed an analytical tool for understanding spine tissues’ behavior in response to vertebral kinematics and spine pathology over a range of body postures. It proposed a novel pipeline of computational models based on predicting individual vertebral kinematics from measurable body-level motions, using musculoskeletal dynamics simulations to drive the vertebrae in corresponding spine FEMs.
A reformulated elastic surface node (ESN) lumbar model was developed for use in MSD simulations. The ESN model modifies the lumbar spine within an existing MSD model by removing non-physiological kinematic constraints and including elastic IVD behavior. The model was scaled using subject-specific anthropometrics and validated to predict in vivo vertebral kinematics and IVD pressures during trunk flexion/extension.
The ESN model was integrated into a novel simulation pipeline that automatically maps it to a kinematics-driven FEM (KD-FEM). The KD-FEM consisted of lumbar vertebrae scaled to subject-specific geometries and actuated by subject-specific vertebral kinematics from the ESN model for different activities. The pipeline was validated for its ability to predict in vivo IVD pressures at L4-L5 level during flexion and load carrying postures.
A detailed multi-layered multi-phase lumbar canal FE model was integrated into the KD-FEM to quantify risks to canal tissues due to vertebral kinematics and progressive canal narrowing (stenosis). This enabled distinct computation of proposed stenosis measures, including cerebrospinal fluid pressure, cauda equina deformation and related stresses/pressure/strains, among others. Model outputs included measures during flexion and comparison of three clinically relevant degrees of progressive stenosis of the bony vertebral foramen at L4 level.
Jaradat, Mohd, "Multi-Scale Vertebral-Kinematics Based Simulation Pipeline of the Human Spine With Application to Spine Tissues Analysis" (2022). All Dissertations. 3100.