Cardiovascular diseases remain the largest disease burden globally, and are the major contributor to disabilities and deaths. Early diagnosis and risk stratification of cardiovascular diseases is crucial to enable timely and targeted treatment. The rapid advancement of imaging and hemodynamic monitoring technologies have made available useful prognostic indicators for early identification of subclinical cardiovascular dysfunction. Nevertheless, these markers, measured at specific locations, are affected by other hemodynamic confounders as a result of hemodynamic coupling. Physics-based computational models, established on the basis of sound theoretical principles, can give insight into the complex interrelationship among different parts of the cardiovascular system and evaluate the sensitivity of these prognostic indicators. To date, diverse types of physics-based cardiovascular models, spanning the disciplines of electrophysiology, electromechanics, solid mechanics, fluid dynamics and cardiovascular reflex, have greatly enhanced our understanding on cardiovascular diseases. However, most of these models, which rely on population-based parameters, have not been widely used in clinical practice due to significant model uncertainties caused by the huge intra- and interpatient variability among patients. Integration of imaging and hemodynamic measurements with physics-based computational models using data assimilation techniques produce personalized cardiovascular models which enable individualized risk prediction and treatment planning. In addition, patient-specific simulations may produce novel, sensitive model-derived parameters for delineation of healthy and pathological conditions, and provide important insights into mechanism underlying disease progression.
This Research Topic focuses on physics-based cardiovascular models encompassing electrophysiology, solid mechanics, fluid dynamics and cardiovascular reflex, which utilize imaging (such as computed tomography or magnetic resonance imaging) or hemodynamic measurements (such as blood pressure or heart rate) for model parameterization or validation. We welcome original, review and meta-analysis research articles, involving:
1) Subject-specific cardiovascular models in the disciplines of electrophysiology, electromechanics, fluid dynamics or reflex regulation for enhanced understanding of cardiovascular diseases as well as individualized risk prediction
2) Parameter estimation techniques for characterizing cardiovascular function or providing important model-derived metrics to delineate healthy and pathological conditions
3) Data-driven, reduced-order cardiovascular models developed based on the availability of experimental measurements
4) Sensitivity analysis and uncertainty quantification techniques for quantifying the impact of measurement error or model parameter variabilities on model predictions
We also welcome any other research articles related to data assimilation and modelling of the cardiovascular system using noninvasive imaging and hemodynamic measurements.
Topic Editor Yubing Shi is receiving a research grant from Alibaba Cloud. Topic Editor Leo Hwa Liang
holds US patent, ‘Membrane for Covering a Peripheral Surface of a Stent’, 20140358221A1. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Cardiovascular diseases remain the largest disease burden globally, and are the major contributor to disabilities and deaths. Early diagnosis and risk stratification of cardiovascular diseases is crucial to enable timely and targeted treatment. The rapid advancement of imaging and hemodynamic monitoring technologies have made available useful prognostic indicators for early identification of subclinical cardiovascular dysfunction. Nevertheless, these markers, measured at specific locations, are affected by other hemodynamic confounders as a result of hemodynamic coupling. Physics-based computational models, established on the basis of sound theoretical principles, can give insight into the complex interrelationship among different parts of the cardiovascular system and evaluate the sensitivity of these prognostic indicators. To date, diverse types of physics-based cardiovascular models, spanning the disciplines of electrophysiology, electromechanics, solid mechanics, fluid dynamics and cardiovascular reflex, have greatly enhanced our understanding on cardiovascular diseases. However, most of these models, which rely on population-based parameters, have not been widely used in clinical practice due to significant model uncertainties caused by the huge intra- and interpatient variability among patients. Integration of imaging and hemodynamic measurements with physics-based computational models using data assimilation techniques produce personalized cardiovascular models which enable individualized risk prediction and treatment planning. In addition, patient-specific simulations may produce novel, sensitive model-derived parameters for delineation of healthy and pathological conditions, and provide important insights into mechanism underlying disease progression.
This Research Topic focuses on physics-based cardiovascular models encompassing electrophysiology, solid mechanics, fluid dynamics and cardiovascular reflex, which utilize imaging (such as computed tomography or magnetic resonance imaging) or hemodynamic measurements (such as blood pressure or heart rate) for model parameterization or validation. We welcome original, review and meta-analysis research articles, involving:
1) Subject-specific cardiovascular models in the disciplines of electrophysiology, electromechanics, fluid dynamics or reflex regulation for enhanced understanding of cardiovascular diseases as well as individualized risk prediction
2) Parameter estimation techniques for characterizing cardiovascular function or providing important model-derived metrics to delineate healthy and pathological conditions
3) Data-driven, reduced-order cardiovascular models developed based on the availability of experimental measurements
4) Sensitivity analysis and uncertainty quantification techniques for quantifying the impact of measurement error or model parameter variabilities on model predictions
We also welcome any other research articles related to data assimilation and modelling of the cardiovascular system using noninvasive imaging and hemodynamic measurements.
Topic Editor Yubing Shi is receiving a research grant from Alibaba Cloud. Topic Editor Leo Hwa Liang
holds US patent, ‘Membrane for Covering a Peripheral Surface of a Stent’, 20140358221A1. The other Topic Editors declare no competing interests with regard to the Research Topic subject.