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The paper addresses methods for generation of individualized computational domains on the basis of medical imaging dataset. The computational domains will be used in one-dimensional (1D) and three-dimensional (3D)-1D coupled hemodynamic models. A 1D hemodynamic model employs a 1D network of a patient-specific vascular network with large number of vessels. The 1D network is the graph with nodes in the 3D space which bears additional geometric data such as length and radius of vessels. A 3D hemodynamic model requires a detailed 3D reconstruction of local parts of the vascular network. We propose algorithms which extend the automated segmentation of vascular and tubular structures, generation of centerlines, 1D network reconstruction, correction, and local adaptation. We consider two modes of centerline representation: (i) skeletal segments or sets of connected voxels and (ii) curved paths with corresponding radii. Individualized reconstruction of 1D networks depends on the mode of centerline representation. Efficiency of the proposed algorithms is demonstrated on several examples of 1D network reconstruction. The networks can be used in modeling of blood flows as well as other physiological processes in tubular structures. Copyright © 2015 John Wiley & Sons, Ltd.

The paper introduces a finite element method for the Navier-Stokes equations of incompressible viscous fluid in a time-dependent domain. The method is based on a quasi-Lagrangian formulation of the problem and handling the geometry in a time-explicit way. We prove that numerical solution satisfies a discrete analogue of the fundamental energy estimate. This stability estimate does not require a CFL time-step restriction. The method is further applied to simulation of a flow in a model of the left ventricle of a human heart, where the ventricle wall dynamics is reconstructed from a sequence of contrast enhanced Computed Tomography images.