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Bioengineering
Special Issues
Cardiovascular Hemodynamic Characterization: Prospects and Challenges
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Cardiovascular Hemodynamic Characterization: Prospects and Challenges

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2522

Special Issue Editors

Prof. Dr. Eduardo Divo

E-Mail Website
Guest Editor
Mechanical Engineering Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: biofluid mechanics; mathematical modeling; boundary element method; mesh reduction method; reduced-order modeling; volume of fluid; optimization schemes; numerical algorithms; multiphysics modeling; in silico and in vitro modeling techniques
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alain Kassab

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: biofluid mechanics; mathematical modeling; boundary element method; mesh reduction method; reduced-order modeling; volume of fluid; optimization schemes; numerical algorithms; in silico and in vitro modeling techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Arka Das

E-Mail Website
Guest Editor
Mechanical Engineering Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: in vitro modeling; biofluid mechanics; experimental flow visualization and tracking techniques; 3D printing techniques; computer vision; instrumentation and controls; machine learning algorithms; multiphysics modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Ray Prather

E-Mail Website
Guest Editor
Mechanical Engineering Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: in silico modeling; computational fluid dynamics; large-eddy simulation; fluid–structure interaction; volume of fluid; biofluid mechanics; cardiovascular, congenital heart defects; multiscale modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiovascular hemodynamics characterization involves the comprehensive study and analysis of the dynamic behavior of blood flow within the cardiovascular system. It aims to understand the intricate interactions between various parameters, such as the structure of the vasculature, blood pressure and properties, flow velocity, shear stress, and wall mechanics. By measuring and analyzing these parameters, researchers can gain an in-depth understanding of the functionality of and potential abnormalities in the cardiovascular system. Characterizing cardiovascular hemodynamics involves the integration of computational modeling, experimental techniques, and clinical observations. In silico models simulate blood flow patterns and interactions within the vasculature, allowing researchers and clinicians to investigate different scenarios and understand how changes in parameters or structures affect hemodynamic behavior. Experimental techniques, such as imaging modalities and flow measurement devices, provide direct observations and measurements of blood flow characteristics in both in vitro and in vivo settings. These experimental data, combined with computational models, enable a more comprehensive characterization of cardiovascular hemodynamics. This Special Issue of Bioengineering showcases the latest developments in computational and experimental modeling techniques, and through research articles and review papers, it aims to present groundbreaking research and advancements in cardiovascular hemodynamics characterization, including its prospects and challenges and encompassing a range of pathologies from healthy to diseased subjects.

Prof. Dr. Eduardo Divo
Prof. Dr. Alain Kassab
Dr. Arka Das
Dr. Ray Prather
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiovascular system
  • biofluid mechanics
  • hemodynamics
  • in-silico modeling
  • computational fluid dynamics
  • in-vitro modeling
  • flow visualization and tracking

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Published Papers (2 papers)

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Research

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18 pages, 4476 KiB  
Article
Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study
by Jiachen Mi, Tengfei Feng, Hongkai Wang, Zuowei Pei and Hong Tang
Bioengineering 2024, 11(8), 842; https://doi.org/10.3390/bioengineering11080842 - 19 Aug 2024
Viewed by 1197
Abstract
Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. [...] Read more.
Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject’s data and tested with another subject’s data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments. Full article
(This article belongs to the Special Issue Cardiovascular Hemodynamic Characterization: Prospects and Challenges)
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Review

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12 pages, 1874 KiB  
Review
Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review
by Kenji Komiya, Shuta Imada, Yoshihiro Ujihara, Shukei Sugita and Masanori Nakamura
Bioengineering 2024, 11(9), 871; https://doi.org/10.3390/bioengineering11090871 - 28 Aug 2024
Viewed by 830
Abstract
Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading [...] Read more.
Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the tension exerted on the vascular wall and making it less likely to rupture. However, desired thrombosis sometimes fails to occur. Predicting thrombus formation can provide valuable information in such cases. This article offers a comprehensive review of conventional methods for predicting thrombus formation. In reviews conducted from the year 2000 to the present, the number of published related papers every five years has increased more than tenfold. We also found that the predictive methods can be classified into two categories: those based on the hemodynamic evaluation parameters and those based on hemodynamic and mathematical models that simulate the transport and reaction of blood components. Through our discussions, we identified several challenges that need to be resolved, including predictions based on patient-specific condition, model validation, multi-scale problems, the mechanisms of thrombus formation, and ensuring cost effectiveness. This review aims to guide researchers interested in exploring thrombus formation prediction within clinical treatments. Full article
(This article belongs to the Special Issue Cardiovascular Hemodynamic Characterization: Prospects and Challenges)
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