About this Research Topic
Computational hemodynamic modelling has been widely applied to quantify the details of blood flow in the human cardiovascular system. Computational models can not only gain quantitative information to remedy the insufficiency/limitations in disease diagnosis via traditional in-vivo measurements but also have the potential to aid in surgical planning or predict the outcomes of treatment. Given the high anatomical complexity of the cardiovascular system and heterogeneous pathogenesis of cardiovascular disease, existing computational models differ largely in terms of geometrical scale, physical principle, and representation of physiopathological factors. So far, reports on successful applications of computational models in real clinical scenarios are rare despite the increasing number of publications in the field. Major challenges in this field mainly include: how to improve the fidelity of in-vivo hemodynamics modeling, how to make full use of available clinical data to personalize models, and how to model the responses of vessels to medical interventions.
The objective of the present Research Topic is to collect original studies or review articles in the field of computational model-aided diagnosis, assessment, or treatment of cardiovascular diseases. These studies are expected to address major challenges in clinical data-based hemodynamic modelling and the utilization of computational models in aiding the management of cardiovascular diseases.
This Research Topic will accept original research and review articles that cover areas including, but not limited to:
1. High-fidelity methods for integrating multimodal image data with hemodynamic models
2. Novel approaches for precise cardiovascular disease diagnosis/evaluation through the combination of computational models and available clinical data
3. Computer model-aided surgical planning and decision making
4. Multi-physics models for predicting the progression of cardiovascular diseases and responses to medical interventions
5. Geometrical multiscale modelling methods for complex hemodynamic phenomena involving local-global hemodynamic interactions
The objective of the present Research Topic is to collect original studies or review articles in the field of computational model-aided diagnosis, assessment, or treatment of cardiovascular diseases. These studies are expected to address major challenges in clinical data-based hemodynamic modelling and the utilization of computational models in aiding the management of cardiovascular diseases.
This Research Topic will accept original research and review articles that cover areas including, but not limited to:
1. High-fidelity methods for integrating multimodal image data with hemodynamic models
2. Novel approaches for precise cardiovascular disease diagnosis/evaluation through the combination of computational models and available clinical data
3. Computer model-aided surgical planning and decision making
4. Multi-physics models for predicting the progression of cardiovascular diseases and responses to medical interventions
5. Geometrical multiscale modelling methods for complex hemodynamic phenomena involving local-global hemodynamic interactions
Keywords: cardiovascular diseases, computational methods, multi-physics model, model personalization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
