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Alves JR, Berg LA, Gaio ED, Rocha BM, de Queiroz RAB, dos Santos RW. A Hybrid Model for Cardiac Perfusion: Coupling a Discrete Coronary Arterial Tree Model with a Continuous Porous-Media Flow Model of the Myocardium. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1229. [PMID: 37628259 PMCID: PMC10453666 DOI: 10.3390/e25081229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
This paper presents a novel hybrid approach for the computational modeling of cardiac perfusion, combining a discrete model of the coronary arterial tree with a continuous porous-media flow model of the myocardium. The constructive constrained optimization (CCO) algorithm captures the detailed topology and geometry of the coronary arterial tree network, while Poiseuille's law governs blood flow within this network. Contrast agent dynamics, crucial for cardiac MRI perfusion assessment, are modeled using reaction-advection-diffusion equations within the porous-media framework. The model incorporates fibrosis-contrast agent interactions and considers contrast agent recirculation to simulate myocardial infarction and Gadolinium-based late-enhancement MRI findings. Numerical experiments simulate various scenarios, including normal perfusion, endocardial ischemia resulting from stenosis, and myocardial infarction. The results demonstrate the model's efficacy in establishing the relationship between blood flow and stenosis in the coronary arterial tree and contrast agent dynamics and perfusion in the myocardial tissue. The hybrid model enables the integration of information from two different exams: computational fractional flow reserve (cFFR) measurements of the heart coronaries obtained from CT scans and heart perfusion and anatomy derived from MRI scans. The cFFR data can be integrated with the discrete arterial tree, while cardiac perfusion MRI data can be incorporated into the continuum part of the model. This integration enhances clinical understanding and treatment strategies for managing cardiovascular disease.
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Affiliation(s)
- João R. Alves
- Department of Education, Federal Institute of Education, Science and Technology of Mato Grosso, Sorriso 78895-150, Brazil
| | - Lucas A. Berg
- Department of Computer Science, Federal Univesity of Juiz de Fora, Juiz de Fora 36036-900, Brazil (E.D.G.); (B.M.R.)
- Department of Computer Science, University of Oxford, Oxford OX3 7LD, UK
| | - Evandro D. Gaio
- Department of Computer Science, Federal Univesity of Juiz de Fora, Juiz de Fora 36036-900, Brazil (E.D.G.); (B.M.R.)
| | - Bernardo M. Rocha
- Department of Computer Science, Federal Univesity of Juiz de Fora, Juiz de Fora 36036-900, Brazil (E.D.G.); (B.M.R.)
| | | | - Rodrigo W. dos Santos
- Department of Computer Science, Federal Univesity of Juiz de Fora, Juiz de Fora 36036-900, Brazil (E.D.G.); (B.M.R.)
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Lourenço WDJ, Reis RF, Ruiz-Baier R, Rocha BM, dos Santos RW, Lobosco M. A Poroelastic Approach for Modelling Myocardial Oedema in Acute Myocarditis. Front Physiol 2022; 13:888515. [PMID: 35860652 PMCID: PMC9289286 DOI: 10.3389/fphys.2022.888515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/27/2022] [Indexed: 12/28/2022] Open
Abstract
Myocarditis is a general set of mechanisms that manifest themselves into the inflammation of the heart muscle. In 2017, more than 3 million people were affected by this disease worldwide, causing about 47,000 deaths. Many aspects of the origin of this disease are well known, but several important questions regarding the disease remain open. One of them is why some patients develop a significantly localised inflammation while others develop a much more diffuse inflammation, reaching across large portions of the heart. Furthermore, the specific role of the pathogenic agent that causes inflammation as well as the interaction with the immune system in the progression of the disease are still under discussion. Providing answers to these crucial questions can have an important impact on patient treatment. In this scenario, computational methods can aid specialists to understand better the relationships between pathogens and the immune system and elucidate why some patients develop diffuse myocarditis. This paper alters a recently developed model to study the myocardial oedema formation in acute infectious myocarditis. The model describes the finite deformation regime using partial differential equations to represent tissue displacement, fluid pressure, fluid phase, and the concentrations of pathogens and leukocytes. A sensitivity analysis was performed to understand better the influence of the most relevant model parameters on the disease dynamics. The results showed that the poroelastic model could reproduce local and diffuse myocarditis dynamics in simplified and complex geometrical domains.
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Affiliation(s)
- Wesley de Jesus Lourenço
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Ruy Freitas Reis
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Ricardo Ruiz-Baier
- School of Mathematics and Victorian Heart Institute, Monash University, Melbourne, VIC, Australia
- Research Core on Natural and Exact Sciences, Universidad Adventista de Chile, Chillán, Chile
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Bernardo Martins Rocha
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Rodrigo Weber dos Santos
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Marcelo Lobosco
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- *Correspondence: Marcelo Lobosco,
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Di Gregorio S, Vergara C, Pelagi GM, Baggiano A, Zunino P, Guglielmo M, Fusini L, Muscogiuri G, Rossi A, Rabbat MG, Quarteroni A, Pontone G. Prediction of myocardial blood flow under stress conditions by means of a computational model. Eur J Nucl Med Mol Imaging 2022; 49:1894-1905. [PMID: 34984502 DOI: 10.1007/s00259-021-05667-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/18/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. METHODS A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. RESULTS Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. CONCLUSION The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
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Affiliation(s)
| | - Christian Vergara
- LABS, Dipartimento Di Chimica, Materiali E Ingegneria Chimica, Politecnico Di Milano, Milan, Italy
| | | | - Andrea Baggiano
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy
| | - Paolo Zunino
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Milan, Italy
| | - Marco Guglielmo
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
| | - Laura Fusini
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Muscogiuri
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital, Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Mark G Rabbat
- Loyola University of Chicago, Chicago, IL, USA
- Edward Hines Jr. VA Hospital, Hines, IL, USA
| | - Alfio Quarteroni
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Milan, Italy
- Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gianluca Pontone
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy.
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Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model. Ann Biomed Eng 2020; 49:1432-1447. [PMID: 33263155 PMCID: PMC8057976 DOI: 10.1007/s10439-020-02681-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 10/25/2020] [Indexed: 11/26/2022]
Abstract
Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data. The goal here is to develop a multiscale patient-specific model enabling blood flow simulation from large coronary arteries to myocardial tissue. Patient vasculatures are segmented from coronary computed tomography angiography data and extended from the image-based model down to the arteriole level using a space-filling forest of synthetic trees. Blood flow is modeled by coupling a 1D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results on five patients with non-obstructive coronary artery disease compare overall well to [\documentclass[12pt]{minimal}
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\begin{document}$$\text {H}_{{2}}$$\end{document}H2O PET exam data for both resting and hyperemic conditions. Results on a patient with severe obstructive disease link coronary artery narrowing with impaired myocardial blood flow, demonstrating the model’s ability to predict myocardial regions with perfusion deficit. This is the first report of a computational model for simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium applied to and validated on human data.
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