About this Research Topic
Currently, hemodynamically guided diagnosis and treatment of cardiovascular diseases has vastly improved morbidity and mortality. However, many challenges remain, such as the increasing complexity of patients' conditions, the uneven level of overall treatment, the heavy task of medical and nursing staff in treating patients, and the accelerated updating and iteration of new technologies in the discipline.
In clinic settings, obtaining direct clinical access of hemodynamic parameters remains challenging, mainly due to the potential risks of invasive measurements and expensive medical costs. Numerous experimental and simulation methods have been developed to address this deficiency to achieve noninvasive detection of hemodynamics. Two commonly utilized techniques are the representative particle image velocimetry (PIV) and computational fluid dynamics (CFD).
Despite these advancements, various technical difficulties persist, including the distortion of silicone models that do not have elastic deformation when replacing real blood vessels, the difficulties in fabrication of flexible polyvinyl alcohol hydrogel (PVA-H) based materials, and the huge computational costs required for CFD simulations, especially when patient-specific boundary conditions are involved or when blood-vessel wall interactions are considered. By overcoming these challenges, we can expand the use of these methods to improve patient care and outcomes. Based on the above background, this research topic deals with novel translational research and state-of-the-art methods for hemodynamic acquisition, including but not limited to:
- New applications of CFD in hemodynamic simulation.
- Sharing of advanced computational tools and cutting-edge algorithms for CFD (e.g., fluid-structure interaction).
- The latest applications of artificial intelligence in hemodynamic acquisition, including machine learning/deep learning applications in clinical and simulation settings.
- Novel methods for assisted processing of medical images.
- Experimental methods such as PIV for estimating cardiovascular flow fields.
- Improved design of interventional devices such as stents.
- Potential of materials science (e.g., polyvinyl alcohol hydrogels) in the treatment of cardiovascular disease.
- Cellular experiments and microscopic hemodynamic analyses (e.g., in vivo, in vitro observation of vascular tissue).
In clinic settings, obtaining direct clinical access of hemodynamic parameters remains challenging, mainly due to the potential risks of invasive measurements and expensive medical costs. Numerous experimental and simulation methods have been developed to address this deficiency to achieve noninvasive detection of hemodynamics. Two commonly utilized techniques are the representative particle image velocimetry (PIV) and computational fluid dynamics (CFD).
Despite these advancements, various technical difficulties persist, including the distortion of silicone models that do not have elastic deformation when replacing real blood vessels, the difficulties in fabrication of flexible polyvinyl alcohol hydrogel (PVA-H) based materials, and the huge computational costs required for CFD simulations, especially when patient-specific boundary conditions are involved or when blood-vessel wall interactions are considered. By overcoming these challenges, we can expand the use of these methods to improve patient care and outcomes. Based on the above background, this research topic deals with novel translational research and state-of-the-art methods for hemodynamic acquisition, including but not limited to:
- New applications of CFD in hemodynamic simulation.
- Sharing of advanced computational tools and cutting-edge algorithms for CFD (e.g., fluid-structure interaction).
- The latest applications of artificial intelligence in hemodynamic acquisition, including machine learning/deep learning applications in clinical and simulation settings.
- Novel methods for assisted processing of medical images.
- Experimental methods such as PIV for estimating cardiovascular flow fields.
- Improved design of interventional devices such as stents.
- Potential of materials science (e.g., polyvinyl alcohol hydrogels) in the treatment of cardiovascular disease.
- Cellular experiments and microscopic hemodynamic analyses (e.g., in vivo, in vitro observation of vascular tissue).
Keywords: Hemodynamic, computational fluid dynamics, artificial Intelligence, interventional devices, materials science
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