Personalized Hemodynamic Modeling of the Human Cardiovascular System: A Reduced-Order Computing Model

Zhang, Xiangdong; Wu, Dan; Miao, Fen; H, Liu; Li, Ye

doi:10.1109/tbme.2020.2970244

Cited by 27 publications

(36 citation statements)

References 35 publications

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“…In view of this, the elastic wall law is adopted in this manuscript to define the behavior of fluid–solid coupling:

(), P ((), A) = P_{0} + β true(\sqrt{A} - \sqrt{A_{0}}) .

where

A_{0}

represents the cross‐sectional area when the pressure is equal to the reference pressure

P_{0}

β

is the stiffness parameter, which can be calculated by elastic modulus E , wall thickness h and

A_{0}

β = \frac{4}{3} \frac{\sqrt{π} italicEh}{A_{0}} .

For the proximal end, an approximate flow boundary is applied to the aortic inlet, which can be determined by the stroke volume (SV) and the left ventricular ejection time (LVET) 23,24 . And for the distal boundary, a three‐element Windkessel model is used to describe the behavior of peripheral vessels:

\frac{d ((), P - Z_{C} Q)}{dt} = \frac{Q}{C} - \frac{P - Z_{C} Q}{RC} .

where

Z_{C}

R

, and

C

represent the proximal resistance, distal resistance, and capacitance of the peripheral vessels, respectively;

Q

is the mass flow rate, which is equal to

italicAU

.…”

Section: Models and Methodsmentioning

confidence: 99%

“…It is believed that the vascular wall has viscoelastic properties, 16,[44][45][46] while in order to simplify the model, some scholars use an elastic law to approximate this characteristic and also achieve satisfactory simulation accuracy. 12,23,42 In view of this, the elastic wall law is adopted in this manuscript to define the behavior of fluid-solid coupling:…”

Section: A Reduced-order Hemodynamic Modelmentioning

confidence: 99%

See 1 more Smart Citation

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

Zhou

et al. 2021

Numer Methods Biomed Eng

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Precise model personalization is a key step towards the application of cardiovascular physical models. In this manuscript, we propose to use deep learning (DL) to solve the parameter estimation problem in cardiovascular hemodynamics. Based on the convolutional neural network (CNN) and fully connected neural network (FCNN), a multi-input deep neural network (DNN) model is developed to map the nonlinear relationship between measurements and the parameters to be estimated. In this model, two separate network structures are designed to extract the features of two types of measurement data, including pressure waveforms and a vector composed of heart rate (HR) and pulse transit time (PTT), and a shared structure is used to extract their combined dependencies on the parameters.Besides, we try to use the transfer learning (TL) technology to further strengthen the personalized characteristics of a trained-well network. For assessing the proposed method, we conducted the parameter estimation using synthetic data and in vitro data respectively, and in the test with synthetic data, we evaluated the performance of the TL algorithm through two individuals with different characteristics. A series of estimation results show that the estimated parameters are in good agreement with the true values. Furthermore, it is also found that the estimation accuracy can be significantly improved by a multicycle combination strategy. Therefore, we think that the proposed method has the potential to be used for parameter estimation in cardiovascular hemodynamics, which can provide an immediate, accurate, and sustainable personalization process, and deserves more attention in the future.

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“…In view of this, the elastic wall law is adopted in this manuscript to define the behavior of fluid–solid coupling:

(), P ((), A) = P_{0} + β true(\sqrt{A} - \sqrt{A_{0}}) .

where

A_{0}

represents the cross‐sectional area when the pressure is equal to the reference pressure

P_{0}

β

is the stiffness parameter, which can be calculated by elastic modulus E , wall thickness h and

A_{0}

β = \frac{4}{3} \frac{\sqrt{π} italicEh}{A_{0}} .

\frac{d ((), P - Z_{C} Q)}{dt} = \frac{Q}{C} - \frac{P - Z_{C} Q}{RC} .

where

Z_{C}

R

, and

C

represent the proximal resistance, distal resistance, and capacitance of the peripheral vessels, respectively;

Q

is the mass flow rate, which is equal to

italicAU

.…”

Section: Models and Methodsmentioning

confidence: 99%

Section: A Reduced-order Hemodynamic Modelmentioning

confidence: 99%

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

Zhou

et al. 2021

Numer Methods Biomed Eng

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show abstract

“…Often physiological data (in the form of blood pressure, blood flow rate, geometrical details, and other measurements) is also fed into the framework to further personalize these models. Most patient-centered cardiovascular simulation tools use a combination of these modelling approaches where a portion of the parameters are derived from physical principles while others are tuned based on patient specific data [42,48,102,181,182]. The general technique for parameter identification and estimation of lumped 0-D models is to tune model parameters so that the difference between the resulting output data and the measured patient specific data is minimized.…”

Section: Personalization Algorithms For Lumped Parameter Modelsmentioning

confidence: 99%

“…8 Panel C). Zhang et al [181] also used the same Levenberg-Marquardt algorithm to personalize a 1D/0D multiscale model of the major cardiovascular vessels using only non-invasive ultrasound and brachial blood pressure measurements from 62 volunteers. Figure 8 (Panel B) illustrates the general patient specific data collection and blood pressure tuning approach.…”

Section: Personalization Algorithms For Lumped Parameter Modelsmentioning

confidence: 99%

The Critical Role of Lumped Parameter Models in Patient-Specific Cardiovascular Simulations

Garber

Khodaei

Keshavarz‐Motamed

2021

Arch Computat Methods Eng

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Cardiovascular (CV) disease impacts tens of millions of people annually and carries a massive global economic burden. Continued advances in medical imaging, hardware and computational efficiency are leading to an increased interest in the field of cardiovascular computational modelling to help combat the devastating impact of CV disease. This review article will focus on a computational modelling technique known as lumped parameter modelling (LPM). Due to its rapid computation time, ease of automation and relative simplicity, LPM holds the potential of aiding in the early diagnosis of CV disease, assisting clinicians in determining personalized and optimal treatments and offering a unique in-silico setting to study cardiac and circulatory diseases. In addition, it is one of the many tools that are needed in the eventual development of patient specific cardiovascular "digital twin" frameworks. This review focuses on how the personalization of cardiovascular lumped parameter models are beginning to impact the field of patient specific cardiovascular care. It presents an in-depth examination of the approaches used to develop current predictive LPM hemodynamic frameworks as well as their applications within the realm of cardiovascular disease. The roles of these models in higher order blood flow (1D/3D) simulations are also explored in addition to the different algorithms used to personalize the models. The article outlines the future directions of this field and the current challenges and opportunities related to the translation of this technology into clinical settings.

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“…The cardiovascular system consists of three parts: the heart, the blood, and the blood vessels [136]. The heart generates pressure to circulate blood throughout the body [137].…”

Section: Introductionmentioning

confidence: 99%

Microfluidic Point-of-Care (POC) Devices in Early Diagnosis: A Review of Opportunities and Challenges

Yang

Zhang

et al. 2022

Sensors

110

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The early diagnosis of infectious diseases is critical because it can greatly increase recovery rates and prevent the spread of diseases such as COVID-19; however, in many areas with insufficient medical facilities, the timely detection of diseases is challenging. Conventional medical testing methods require specialized laboratory equipment and well-trained operators, limiting the applicability of these tests. Microfluidic point-of-care (POC) equipment can rapidly detect diseases at low cost. This technology could be used to detect diseases in underdeveloped areas to reduce the effects of disease and improve quality of life in these areas. This review details microfluidic POC equipment and its applications. First, the concept of microfluidic POC devices is discussed. We then describe applications of microfluidic POC devices for infectious diseases, cardiovascular diseases, tumors (cancer), and chronic diseases, and discuss the future incorporation of microfluidic POC devices into applications such as wearable devices and telemedicine. Finally, the review concludes by analyzing the present state of the microfluidic field, and suggestions are made. This review is intended to call attention to the status of disease treatment in underdeveloped areas and to encourage the researchers of microfluidics to develop standards for these devices.

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Personalized Hemodynamic Modeling of the Human Cardiovascular System: A Reduced-Order Computing Model

Cited by 27 publications

References 35 publications

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

The Critical Role of Lumped Parameter Models in Patient-Specific Cardiovascular Simulations

Microfluidic Point-of-Care (POC) Devices in Early Diagnosis: A Review of Opportunities and Challenges

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