Novel machine-learning methodology refines simulation of heart anatomy and function

A rapid optimization technique for mitral-valve modeling is applicable to other cardiac structures.

Reza Salari, Thornton Tomasetti Applied Science

Preselected matching points are used to measure the difference between the simulated and target shapes of a diseased mitral valve. (Image courtesy of Thornton Tomasetti)

The medical device industry is making great strides in finite element analysis (FEA), a fundamental tool underlying simulation-guided product development. FEA enables evaluation and prediction of the behavior of implanted medical devices and their interactions with human tissues. Yet going from accurate modeling of normal organ and tissue behavior to patient- and/or disease-specific simulations remains a significant challenge.

When evaluating heart disease with simulation, one approach to creating diseased-state models is to generate 3D models and FEA meshes from patient-specific imaging data such as CT scans and/or MRI. While the associated segmentation software has improved significantly, this process can still be extremely time-consuming. It also may not capture all of the details required for the simulated behavior to match the true diseased state, especially when there is motion involved, as is the case with a beating heart.

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