This week I’ve been in Odense for a PhD defense (Congratulations to Dr. Christian Valdemar Hansen!), while today I am giving at a talk at Nansensenteret i Bergen for a meeting organized by Norges Tekniske Vitenskapsakademi and Tekna. I will talk about the brain’s waterscape/Hjernens vannveier, of course. This presentation targets a semi-academic, semi-technical audience and the slides are publicly available.
Computational cardiac modelling is currently evolving from a pure research tool to aiding in clinical decision making. Assessing the reliability of computational model predictions is a key factor for clinical use, and uncertainty quantification (UQ) and sensitivity analysis are important parts of such an assessment. In this study, we apply new methods for UQ in computational heart mechanics to study uncertainty both in material parameters characterizing global myocardial stiffness and in the local muscle fiber orientation that governs tissue anisotropy. The uncertainty analysis is performed using the polynomial chaos expansion (PCE) method, which is a non-intrusive meta-modeling technique that surrogates the original computational model with a series of orthonormal polynomials over the random input parameter space. In addition, in order to study variability in the muscle fiber architecture, we model the uncertainty in orientation of the fiber field as an approximated random field using a truncated Karhunen-Loéve expansion. The results from the UQ and sensitivity analysis identify clear differences in the impact of various material parameters on global output quantities. Furthermore, our analysis of random field variations in the fiber architecture demonstrate a substantial impact of fiber angle variations on the selected outputs, highlighting the need for accurate assignment of fiber orientation in computational heart mechanics models.
Congratulations to Ada Ellingsrud for winning the Best Poster Award at last week’s 3rd Human Brain Project School on Future Neuroscience! Ada, PhD candidate with the Waterscales project, presented her exciting research plan on developing computational models of astrocyte water dynamics to explore how osmotic pressure and microscopic fluid flow impact movement of metabolic waste and solutes in the parenchyma. Her poster is available on figshare: Modelling the role of glial cells in cerebral interstitial fluid movement .
Our paper titled Numerical study of intrathecal drug delivery to a permeable spinal cord: effect of catheter position and angle is now published. Intrathecal delivery is a procedure involving the release of therapeutic agents into the cerebrospinal fluid (CSF) through a catheter. It holds promise for treating high-impact central nervous system pathologies, for which systemic administration routes are ineffective. In this study we introduce a numerical model able to simultaneously account for solute transport in the fluid and in the spinal cord. Using a Discontinuous Galerkin method and a three-dimensional patient-specific geometry, we studied the effect of catheter position and angle on local spinal cord drug concentration. Based on our simulations, lateral injection perpendicular to the cord turned out to be more effective than other configurations.
Our paper on A Cell-Based Framework for Numerical Modeling of Electrical Conduction in Cardiac Tissue is now (open access) published! In this paper, we study a mathematical model of cardiac tissue based on explicit representation of individual cells. In this EMI model, the extracellular (E) space, the cell membrane (M), and the intracellular (I) space are represented as separate geometrical domains. This representation introduces modeling flexibility needed for detailed representation of the properties of cardiac cells including their membrane. In particular, we will show that the model allows ion channels to be non-uniformly distributed along the membrane of the cell. Such features are difficult to include in classical homogenized models like the monodomain and bidomain models frequently used in computational analyses of cardiac electrophysiology.
Our paper on High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle is now published! Computational models of cardiac mechanics, personalized to a patient, offer access to mechanical information above and beyond direct medical imaging. Additionally, such models can be used to optimize and plan therapies in-silico, thereby reducing risks and improving patient outcome. Model personalization has traditionally been achieved by data assimilation, which is the tuning or optimization of model parameters to match patient observations. Current data assimilation procedures for cardiac mechanics are limited in their ability to efficiently handle high-dimensional parameters. This restricts parameter spatial resolution, and thereby the ability of a personalized model to account for heterogeneities that are often present in a diseased or injured heart. In this paper, we address this limitation by proposing an adjoint gradient–based data assimilation method that can efficiently handle high-dimensional parameters. We test this procedure on a synthetic data set and provide a clinical example with a dyssynchronous left ventricle with highly irregular motion. Our results show that the method efficiently handles a high-dimensional optimization parameter and produces an excellent agreement for personalized models to both synthetic and clinical data.
The European Conference on Numerical Mathematics and Advanced Applications (ENUMATH) was organized by the University of Bergen at Voss this week, Sept 25-29 2017. The Waterscapes research teams were well represented at the conference with the plenary open lecture on “The operator preconditioning framework with various applications to interstitial fluid flow and the aging human brain” given by Waterscape collaborator Kent-Andre Mardal, and I presenting a plenary lecture on “Compatible discretizations in our hearts and mind”. Slides available on SlideShare: