@article {26202,
	title = {Artery.FE: An implementation of the 1D blood flow equations in FEniCS},
	journal = {Journal of Open Source Software},
	volume = {3},
	number = {1107},
	year = {2018},
	publisher = {The Journal of Open Source Software, Open Source Initiative},
	keywords = {blood flow, FEniCS, Numerical models, PDE, Software},
	doi = {10.21105/joss.01107},
	url = {http://joss.theoj.org/papers/10.21105/joss.01107},
	author = {Syver D. {Agdestein} and Valen-Sendstad, Kristian and Alexandra K. {Diem}}
}
@misc {26066,
	title = {Modelling drug delivery via nanoparticle deposition in the myocardium of the left ventricle},
	year = {2018},
	publisher = {The Heart by Numbers: Integrating Theory, Computation, and Experiment to Advance Cardiology},
	address = {Berlin},
	abstract = {The use of nanoparticles (NP) target drug delivery directly to the heart for treatment of diseases via nanoparticles (NP) has been a major goal of cardiovascular research since the early 2000s. The benefits of such a NP drug delivery system would include the reduction of side effects by the administration of smaller dosages, cost reduction and reducing the need for invasive treatments. However, the development of a NP-based drug delivery system also poses a number of challenges, such as the optimisation of physico-chemical parameters of the NP, in order to achieve efficient distribution throughout the tissue. We address this challenge by presenting a finite-element model of NP delivery via perfusion through the myocardium in the left ventricle (LV). Perfusion is represented by the three compartment porous media equations based on Darcy{\textquoteright}s law. A 0D lumped parameter model is used to represent the inflow boundary condition to the perfusing blood vessels. NP transport is modelled via the scalar transport equations based on reaction-advection-diffusion kinetics, where deposition via endocytosis follows zero order reaction kinetics. The model is solved on a human LV geometry with randomly set arterial entry points throughout the outer surface of the myocardium. Efficiency of NP endocytosis is tested based on varied kinetic rates, initial NP concentration, NP inflow rate, chemical properties of NP, and perfusion pressure. These simulations provide a framework to virtually prototype physico-chemical properties of the NP and predict their distribution within the tissue.},
	keywords = {blood flow, cardiac perfusion, darcy flow, nanoparticles},
	author = {Alexandra K. {Diem} and Valen-Sendstad, Kristian}
}
@article {25229,
	title = {Variational data assimilation for transient blood flow simulations},
	journal = { International Journal for Numerical Methods in Biomedical Engineering},
	volume = {35},
	year = {2018},
	month = {10/2018},
	pages = {e3152},
	publisher = {John Wiley \& Sons},
	abstract = {Several cardiovascular diseases are caused from localised abnormal blood flow\&nbsp;such as in the case of stenosis or aneurysms. Prevailing theories propose that the development is caused by abnormal wall-shear stress in focused\&nbsp;areas. \&nbsp;Computational fluid mechanics have arisen as a promising tool for\&nbsp;a more precise and quantitative analysis, in particular because the\&nbsp;anatomy is often readily available even by standard imaging techniques such as magnetic resolution and computed tomography angiography. \&nbsp;However,\&nbsp;computational fluid mechanics rely on accurate initial and boundary conditions which\&nbsp;is difficult to obtain. \&nbsp;In this paper we address the problem of recovering high resolution information from noisy, low-resolution\&nbsp;measurements of blood flow using variational data assimilation based on a transient Navier-Stokes model.\&nbsp;Numerical experiments are performed in both 2D and 3D and with pulsatile flow relevant for physiological flow in cerebral aneurysms.\&nbsp;The results demonstrate that, with suitable regularisation, the model accurately reconstructs flow, even in the presence of significant noise.\&nbsp;},
	keywords = {adjoint equations, blood flow, Finite element method, Navier-Stokes, optimal control, variational data assimilation},
	author = {Simon W {Funke} and Nordaas, Magne and Evju, {\O}yvind and Martin Sandve {Aln{\ae}s} and Mardal, Kent-Andre}
}
