@article{15323,
  author = {Alexandra Diem and Kristian Valen-Sendstad},
  title = {Time-Lapsing Perfusion: Proof of Concept of a Novel Method to Study Drug Delivery in Whole Organs},
  abstract = {Perfusion is one of the most important processes maintaining organ health. From a computational perspective, however, perfusion is amongst the least studied physiological processes of the heart. The recent development of novel nanoparticle-based targeted cardiac therapy calls for novel simulation methods that can provide insights into the distribution patterns of therapeutic agents within the heart tissue. Additionally, resolving the distribution patterns of perfusion is crucial for gaining a full understanding of the long-term impacts of cardiovascular diseases that can lead to adverse remodelling, such as myocardial ischemia and heart failure. In this study we have developed and used a novel particle tracking-based method to simulate the perfusion-mediated distribution of nanoparticles or other solutes. To model blood flow through perfused tissue we follow the approach of others and treat the tissue as a porous medium in a continuum model. Classically, solutes are modelled using reaction-advection-diffusion kinetics. However, due to the discrepancy between advection and diffusion in blood vessels, this method becomes numerically unstable. Instead, we track a bolus of solutes or nanoparticles using particle tracking based purely on advection in arteries. In capillaries we employ diffusion kinetics, using an effective diffusion coefficient to mimic capillary blood flow. We first demonstrate the numerical validity and computational efficiency of this method on a 2D benchmark problem. Finally, we demonstrate how the method is used to visualise perfusion patterns of a human left ventricle geometry. The efficiency of the method allows for nanoparticle tracking over multiple cardiac cycles using a conventional laptop, providing a framework for the simulation of experimentally relevant time frames to advance pre-clinical research.},
  year = {2019},
  journal = {Biophysical Journal},
  volume = {117},
  pages = {2316-2323},
  publisher = {Cell Press},
}