We'll present a deep learning-based analysis framework for making key decisions about heart valve replacement and valve design. We'll describe how we use deep learning to predict valve performance measures, which makes these measurements accessible to physicians who lack expert computational knowledge. We will explain how our trained DL framework can be used interactively to predict valve-performance measures with the same fidelity as time-consuming biomechanics simulations. We'll also discuss how our tool can help doctors with heart valve diagnosis, ultimately improving patient care.
We present new GPU algorithms for computing the directed Hausdorff distance between freeform surfaces, with applications in shape matching, mesh simplification, and geometric approximation and optimization. Our algorithms run in real-time with very small error bounds for parametric models defined by complex NURBS surfaces and can be used to interactively compute the Hausdorff distance for models made of dynamic deformable surfaces. We discuss implementation decisions and tradeoffs between OpenGL, Cuda, and Thrust, and the advantages and disadvantages of parallel hierarchical culling methods for this application.