A Simple, Efficient Method for an Automatic Adjustment of the Lumbar Curvature Alignment in an MBS Model of the Spine

Abstract

In many fields of spinal health care, efforts have been made to offer individualized products and therapy tailored to the patient. Therefore, the prevailing alignment of the spine must be considered, which varies from person to person and depends on the movement and loading situation. With the help of patient-specific simulation models of the spine, the geometrical parameters in a specific body position can be analyzed, and the load situation of the spinal structures during dynamic processes can be assessed. However, to enable the future usability of such simulation models in medical reality, as many patient-specific conditions as possible need to be considered. Another critical requirement is that simulation models must be quickly and easily created for use in clinical routine. Building new or adapting existing spine multibody simulation (MBS) models is time-consuming due to their complex structure. To overcome this limitation, we developed a simple, efficient method by which to automatically adjust the lumbar curvature orientation of the spine model. The method extracts a new 3D lordosis curve from patient-specific data in the preprocessing step. Then the vertebrae and all linked spinal structures of an existing spinal simulation model are transformed so that the lumbar lordosis follows the curve obtained in the first part of the method. To validate the proposed approach, three independent experts measured the Cobb angle in the source and the generated spine alignments. We calculated a mean absolute error of 1.29° between the generated samples and the corresponded ground truth. Furthermore, the minor deviation in the root mean square error (RMSE) of 0.0012 m2 between the areas under the alignment curves in the original and target lordosis curvatures indicated the accuracy of the proposed method. The proposed method demonstrated that a new patient-specific simulation model can be generated in a short time from any suitable data source.