During the final year of my Master’s, I was working at Siemens Corporate Technology in Princeton, NJ, USA, on a very interesting project dealing with various aspects of electrophysiology and biomechanics models of the human heart muscle. My time there was one of my most productive phases so far, with not only the successful completion of my MSc thesis titled “Efficient and Robust Patient-Specific Model of the Heart Function based on MRI Images” but also a great publication outcome (see below).
In my work, fast and robust patient-specific parameter estimation for a biomechanic model of the human heart from clinical and imaging data is investigated. Of course, my results are based on available models of heart anatomy and electrophysiology, and – working in a great team at Siemens – I could heavily benefit from extensive experience in heart segmentation and model generation.
My thesis has two major contributions: First, an integrated framework to compute cardiac motion using a finite element setup is presented, in particular including an efficient strategy to parallelize the evaluation of stress and mechanical boundary conditions for high-performance implementations. Second, a novel, data-driven approach to calibrate electrophysiology (EP) parameters from clinically available 12-lead electrocardiograms (ECGs) is introduced, as illustrated in the figure.
For more details, please refer to the following publications: