Unveiling interactions between intervertebral disc morphologies and mechanical behavior through personalized finite element modeling.

Introduction: Intervertebral Disc (IVD) Degeneration (IDD) is a significant health concern, potentially influenced by mechanotransduction. However, the relationship between the IVD phenotypes and mechanical behavior has not been thoroughly explored in local morphologies where IDD originates. This work unveils the interplays among morphological and mechanical features potentially relevant to IDD through Abaqus UMAT simulations. Methods: A groundbreaking automated method is introduced to transform a calibrated, structured IVD finite element (FE) model into 169 patient-personalized (PP) models through a mesh morphing process. Our approach accurately replicates the real shapes of the patient's Annulus Fibrosus (AF) and Nucleus Pulposus (NP) while maintaining the same topology for all models. Using segmented magnetic resonance images from the former project MySpine, 169 models with structured hexahedral meshes were created employing the Bayesian Coherent Point Drift++ technique, generating a unique cohort of PP FE models under the Disc4All initiative. Machine learning methods, including Linear Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression, were used to explore correlations between IVD morphology and mechanics. Results: We achieved PP models with AF and NP similarity scores of 92.06\% and 92.10\% compared to the segmented images. The models maintained good quality and integrity of the mesh. The cartilage endplate (CEP) shape was represented at the IVD-vertebra interfaces, ensuring personalized meshes. Validation of the constitutive model against literature data showed a minor relative error of 5.20%. Discussion: Analysis revealed the influential impact of local morphologies on indirect mechanotransduction responses, highlighting the roles of heights, sagittal areas, and volumes. While the maximum principal stress was influenced by morphologies such as heights, the disc's ellipticity influenced the minimum principal stress. Results suggest the CEPs are not influenced by their local morphologies but by those of the AF and NP. The generated free-access repository of individual disc characteristics is anticipated to be a valuable resource for the scientific community with a broad application spectrum.

3D-DXA Based Finite Element Modelling for Femur Strength Prediction: Evaluation Against QCT.

Osteoporosis is characterised by the loss of bone density resulting in an increased risk of fragility fractures. The clinical gold standard for diagnosing osteoporosis is based on the areal bone mineral density (aBMD) used as a surrogate for bone strength, in combination with clinical risk factors. Finite element (FE) analyses based on quantitative computed tomography (QCT) have been shown to estimate bone strength better than aBMD. However, their application in the osteoporosis clinics is limited due to exposure of patients to increased X-rays radiation dose. Statistical modelling methods (3D-DXA) enabling the estimation of 3D femur shape and volumetric bone density from dual energy X-ray absorptiometry (DXA) scan have been shown to improve osteoporosis management. The current study used 3D-DXA based FE analyses to estimate femur strength from the routine clinical DXA scans and compared its results against 151 QCT based FE analyses, in a clinical cohort of 157 subjects. The linear regression between the femur strength predicted by QCT-FE and 3D-DXA-FE models correlated highly (coefficient of determination R2 = 0.86) with a root mean square error (RMSE) of 397 N. In conclusion, the current study presented a 3D-DXA-FE modelling tool providing accurate femur strength estimates noninvasively, compared to QCT-FE models.