Research

Thesis working title: "Validation and optimization of a workflow to predict bone growth with musculoskeletal simulations and FEM simulations"

Thesis outline: During my PhD thesis I will conduct a longitudinal study about bone growth in typically developing children and children with cerebral palsy (CP) to validate and optimize an existing multi-scale workflow. Children with CP are born with typical musculoskeletal geometry but due to pathological loading of the skeleton the bone geometry does not develop as in typically developing children. With the reliable understanding of bone growth, clinicians could focus on early-stage interventions to normalise gait and bone growth instead of severe surgical interventions.

Three dimensional gait analysis (3DGA) and MRI data will be captured at two data collection sessions, approximately two years apart.

3DGA data is used for a musculoskeletal simulation with OpenSim to calculate muscle forces and joint contact forces. With the help of MRI data, a subject-specific 3D model of the femur is created. This model is then used in finite-element analysis with muscle forces and joint contact forces as boundary conditions to predict bone growth.

The predicted bone growth will be compared to the measured bone growth from the second MRI session. Since the existing workflow is based on many model assumptions there will be some discrepancies.

Finally, adjustments will be made to the workflow to improve the bone growth predictions to get more accurate results.

If the whole workflow can be performed by clinicians with low effort it could be a game changer in clinical decision making. They could predict bone growth and make early interventions to correct the gait pattern and therefore the loading environment to encourage normal bone growth instead of severe end stage surgeries.

Supervisor & Co-Mentor: Hans Kainz, Arnold Baca

Back