Mixed impact of torsion on LV hemodynamics: A CFD study based on the Chimera technique.
Clicks: 304
ID: 42235
2019
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Popular Article
81.5
/100
303 views
241 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
Image-based patient-specific Computational Fluid Dynamics (CFD) models of the Left Ventricle (LV) can be used to quantify hemodynamics-based biomarkers that can support the clinicians in the early diagnosis, follow-up and treatment planning of patients, beyond the capabilities of the current imaging modalities. We propose a workflow to build patient-specific CFD models of the LV with moving boundaries based on the Chimera technique to overcome the convergence issues previously encountered by means of the Arbitrarian Lagrangian Eulerian approach. The workflow was tested while investigating whether the torsional motion has an impact on LV fluid dynamics. Starting from 3D cine MRI scans of a healthy volunteer, six cardiac cycles were simulated in three CFD LV models: with no, physiological, and exaggerated torsion. The Chimera technique was robust in handling the impulsive motion of the LV endocardium, allowing to notice cycle-to-cycle variations in every simulated case. Torsion affected slightly velocity, vorticity, WSS. It did not affect energy loss and induced a double-sided effect in terms of residence time: the particles ejected in one beat decreased, whereas the motility of the particles remaining in the LV was affected only in the exaggerated torsion case, indicating that implementation of torsion can be discarded in case of physiological levels. Nonetheless, caution is warranted when interpreting these results given the absence of the mitral valve, the papillary muscles, and the trabeculae. The effects of the mitral valve will be evaluated within an Fluid Structure Interaction simulation framework as further development of the current model.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (247 words).
Try re-searching for a better abstract.
| Reference Key |
can2019mixedcomputers
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Canè, Federico;Selmi, Matteo;De Santis, Gianluca;Redaelli, Alberto;Segers, Patrick;Degroote, Joris; |
| Journal | Computers in biology and medicine |
| Year | 2019 |
| DOI |
S0010-4825(19)30240-9
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.