Full Description
Advances in Blood Flow Quantification Methods for Diagnosis, Monitoring, Prediction, and Personalized Treatment of Cardiovascular Disease explains how abnormal hemodynamics (blood flow dynamics) greatly alter the cardiovascular system's ability to transport, control and maintain blood flow throughout the body. These deviations often lead to the initiation and progression of disease. Notably, hemodynamic quantification can be extremely useful for accurate and early diagnosis of cardiovascular disease. Particularly, the ability to monitor and predict hemodynamic effects following specific interventions can significantly broaden lifesaving options. However, image-based computational modelling of predictive hemodynamics analysis methods (and therefore new diagnostic tools) are rare. By extending quantitative methods of analysis, medical professionals can foresee effects of various cardiovascular interventions, allowing for optimized, situation-based care.
Advances in Blood Flow Quantification Methods for Diagnosis, Monitoring, Prediction, and Personalized Treatment of Cardiovascular Disease posits that novel, image-based computational modelling hemodynamic analysis tools will enable the next generation of diagnosis, predictive, and treatment planning resources for cardiovascular disease. Readers will become well-versed in recent advances in hemodynamic approaches, while preparing themselves for the next wave of revolutions in cardiovascular health.
Contents
Introduction
Hemodynamics: Fundamentals and roles in atrial fibrillation
Advances in blood flow quantification methods for diagnosis and monitoring of cardiovascular disease
Advances in blood flow quantification methods for prediction of cardiovascular interventions
Computational predictive modeling of valve performance and risks toward personalization of TAVR with lifetime management
Advances in 4D flow MRI blood flow quantification methods for prediction of cardiovascular intervention
Advancing clinical interventions through patient‑specific in‑vitro simulators: Challenges, techniques and future directions
Enhancing patient management with TAVR and coronary disease through computational modeling
Impact of flow in assessment and prognosis of valvular heart diseases
Translational machine learning in cardiac disease: Advancing diagnosis, monitoring, and prediction
Bridging the critical gap in cardiovascular biomechanics: From computational models to clinical applications
