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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2412.04798 |
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| _version_ | 1866913968837099520 |
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| author | Yang, Haizhou Zhang, Jiyang Assi, Ismael Z. Nallamothu, Brahmajee K. Garikipati, Krishna Figueroa, C. Alberto |
| author_facet | Yang, Haizhou Zhang, Jiyang Assi, Ismael Z. Nallamothu, Brahmajee K. Garikipati, Krishna Figueroa, C. Alberto |
| contents | Coronary Microvascular Dysfunction (CMD) is characterized by impaired vasodilation and can lead to insufficient blood flow to the myocardium during stress or exertion, affecting millions of people globally. Despite their diagnostic value, invasive, wire-based diagnosis techniques of CMD, such as index of microcirculatory resistance (IMR) and coronary flow reserve (CFR), are underutilized due to their complexity and inconsistency. Coronary angiography, one of the most commonly used imaging modalities, offers valuable flow information that assists in diagnosing CMD. However, this information is not fully understood or utilized in current clinical practice. In this study, a 3D-0D coupled multi-physics computational fluid dynamics (CFD) model was developed and calibrated to simulate and study the process of contrast injection and washout during clinical angiography. A contrast intensity profile (CIP) was introduced to describe the dynamics of coronary angiography data. Additionally, sensitivity studies were conducted to evaluate the influence of various coronary lumped parameter model (LPM) parameters on the shapes of CIPs. The results demonstrate that the multi-physics model can be effectively calibrated to produce physiologically meaningful hemodynamic results. Sensitivity studies reveal that resistance has a greater impact on the rising and falling slopes of CIP than capacitance, with higher resistance amplifying this effect. The model and results are presented here. These results are potentially transformative, as they provide a tool for interpreting angiographic data and ultimately extracting information concerning coronary microcirculation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-physics Model of Flow from Coronary Angiography: Insights to Microvascular Function Yang, Haizhou Zhang, Jiyang Assi, Ismael Z. Nallamothu, Brahmajee K. Garikipati, Krishna Figueroa, C. Alberto Computational Engineering, Finance, and Science Coronary Microvascular Dysfunction (CMD) is characterized by impaired vasodilation and can lead to insufficient blood flow to the myocardium during stress or exertion, affecting millions of people globally. Despite their diagnostic value, invasive, wire-based diagnosis techniques of CMD, such as index of microcirculatory resistance (IMR) and coronary flow reserve (CFR), are underutilized due to their complexity and inconsistency. Coronary angiography, one of the most commonly used imaging modalities, offers valuable flow information that assists in diagnosing CMD. However, this information is not fully understood or utilized in current clinical practice. In this study, a 3D-0D coupled multi-physics computational fluid dynamics (CFD) model was developed and calibrated to simulate and study the process of contrast injection and washout during clinical angiography. A contrast intensity profile (CIP) was introduced to describe the dynamics of coronary angiography data. Additionally, sensitivity studies were conducted to evaluate the influence of various coronary lumped parameter model (LPM) parameters on the shapes of CIPs. The results demonstrate that the multi-physics model can be effectively calibrated to produce physiologically meaningful hemodynamic results. Sensitivity studies reveal that resistance has a greater impact on the rising and falling slopes of CIP than capacitance, with higher resistance amplifying this effect. The model and results are presented here. These results are potentially transformative, as they provide a tool for interpreting angiographic data and ultimately extracting information concerning coronary microcirculation. |
| title | A Multi-physics Model of Flow from Coronary Angiography: Insights to Microvascular Function |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2412.04798 |