The aim of the Computational Medicine group is to improve the understanding of disease mechanisms, the prediction of disease progress, and the individual planning of complex treatments by mathematical modelling, simulation, and optimization. To this end, we develop efficient adaptive simulation and optimization algorithms for PDE problems in several biomedical applications. The resulting algorithms are combined with software prototypes developed in the Therapy Planning group to form innovative solutions for individualized medicine.
Current research focus are joint diseases, where we compute mechanical loads in joints, optimize implant positions, and simulate implant wear. Further topics are cardiac simulation and medical image acquisition. Increasing complexity of the biomechanical and physiological processes is gaining importance as we move towards multiscale, multiphysics, and hybrid models, coupling, e.g., metabolism to nutrient supply affected by mechanical loading, or myocardial excitation to mechanical contraction, blood flow and tissue remodelling.
Another emerging research topic is quantification of uncertainty in models and data, affecting conclusions drawn from simulations as well as parameter identification and optimization algorithms. The algorithms resulting from research in the group are implemented in the finite element toolbox Kaskade7, a flexible C++ library developed at ZIB.