Generating fiber orientation maps in human heart models using Poisson interpolation
By J. Wong, Ellen Kuhl. 2012
130 citations recorded by the source corpus.
Smoothly varying muscle fibre orientations in the heart are critical to its electrical and mechanical function. From detailed histological studies and diffusion tensor imaging, we now know that fibre orientations in humans vary gradually from approximately − 70° in the outer wall to +80° in the inner wall. However, the creation of fibre orientation maps for computational analyses remains one of the most challenging problems in cardiac electrophysiology and cardiac mechanics. Here, we show that Poisson interpolation generates smoothly varying vector fields that satisfy a set of user-defined constraints in arbitrary domains. Specifically, we enforce the Poisson interpolation in the weak sense using a standard linear finite element algorithm for scalar-valued second-order boundary value problems and introduce the feature to be interpolated as a global unknown. User-defined constraints are then simply enforced in the strong sense as Dirichlet boundary conditions. We demonstrate that the proposed concept is capable of generating smoothly varying fibre orientations, quickly, efficiently and robustly, both in a generic bi-ventricular model and in a patient-specific human heart. Sensitivity analyses demonstrate that the underlying algorithm is conceptually able to handle both arbitrarily and uniformly distributed user-defined constraints; however, the quality of the interpolation is best for uniformly distributed constraints. We anticipate our algorithm to be immediately transformative to experimental and clinical settings, in which it will allow us to quickly and reliably create smooth interpolations of arbitrary fields from data-sets, which are sparse but uniformly distributed.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Cardiology and Cardiovascular Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, methodology): “There are several methodologies to incorporate this information with ruled-based approaches [38, 39] and mapping techniques [40, 41].”
- Automatic mapping of atrial fiber orientations for patient‐specific modeling of cardiac electromechanics using image registration (2018, background): “Due to individual fiber bundles running in different directions it is not straightforward to use rule-based approaches, as it can be done in the ventricles [8], [9].”