ArticleJournal of the Royal Society, Interface2018
Optimization of topological complexity for one-dimensional arterial blood flow models.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 33 citations in OpenAlex.
- A Computationally Efficient and Causal Frequency Domain Formalism for Hemodynamics Allowing for Nonlinearities and Generalized Coupling Conditions.International journal for numerical methods in biomedical engineering · 2025Article
- A coupling strategy for a first 3D-1D model of the cardiovascular system to study the effects of pulse wave propagation on cardiac function.Computational mechanics · 2022Article
- Automated model calibration with parallel MCMC: Applications for a cardiovascular system model.Frontiers in physiology · 2022Article
- Estimating central blood pressure from aortic flow: development and assessment of algorithms.American journal of physiology. Heart and circulatory physiology · 2021Article
- Estimating Central Pulse Pressure From Blood Flow by Identifying the Main Physical Determinants of Pulse Pressure Amplification.Frontiers in physiology · 2021Article
- Complexus cordisArchivos de cardiologia de Mexico · 2020Article
- Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries.Journal of the Royal Society, Interface · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
As computational models of the cardiovascular system are applied in modern personalized medicine, maximizing certainty of model input becomes crucial. A model with a high number of arterial segments results in a more realistic description of the system, but also requires a high number of parameters with associated uncertainties. In this paper, we present a method to optimize/reduce the number of arterial segments included in one-dimensional blood flow models, while preserving key features of flow and pressure waveforms. We quantify the preservation of key flow features for the optimal network with respect to the baseline networks (a 96-artery and a patient-specific coronary network) by various metrics and quantities like average relative error, pulse pressure and augmentation pressure. Furthermore, various physiological and pathological states are considered. For the aortic root and larger systemic artery pressure waveforms a network with minimal description of lower and upper limb arteries and no cerebral arteries, sufficiently captures important features such as pressure augmentation and pulse pressure. Discrepancies in carotid and middle cerebral artery flow waveforms that are introduced by describing the arterial system in a minimalistic manner are small compared with errors related to uncertainties in blood flow measurements obtained by ultrasound.
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Registered trials
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