A computational model applied to myocardial perfusion in the human heart: From large coronaries to microvasculature

Gregorio, Simone Di; Fedele, Marco; Corno, Antonio F.; Zunino, Paolo; Vergara, Christian; Quarteroni, Alfio

doi:10.1016/j.jcp.2020.109836

Cited by 33 publications

(50 citation statements)

References 78 publications

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“…Moreover, the meshing tools proposed in this paper have also been used in other works regarding the cardiac electrophysiology, 93,94 the cardiac electromechanics, 95 and the cardiac perfusion 96 . Finally, we remark that also electro‐mechanics and electro‐mechano‐fluid models of the whole heart are currently under development.…”

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 84%

“…The proposed algorithms have been combined into ad‐hoc pipelines to test them in some complex cardiac applications, like the mesh generation of a fully‐detailed ventricular geometry including the papillary muscles and the trabeculae carneae (Section 3.2) or a complete fluid‐dynamics mesh of the left‐heart (Section 3.3). Moreover, as discussed in Section 3.4, they have already been successfully used in other papers to address a broad range of applications 28,91‐96 . These examples highlight the flexibility of these tools and their ability to be easily applicable to a large variety of cardiac mesh generations through the building of ad‐hoc application‐dependent pipelines.…”

Section: Discussionmentioning

confidence: 98%

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Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

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Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 98%

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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“…The first class of algorithms can easily follow statistical models of the main morphometric measures (vessel radius, length, aspect ratio and bifurcation angle), regardless of the shape of the vascular territory 31 – 35 . The second class of algorithms allows one to generate a network of vessels inside a vascular territory defined in (2D or 3D) space 23 , 36 – 47 . A particular class of such space-filling algorithms is termed Constrained Constructive Optimisation (CCO) because the sequential generation of the vessel network is driven by the constrained minimisation of a cost function.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, cases of territories supplied by multiple inlet vessels were the focus of attempts to tackle more realistic scenarios. In Blanco et al 45 , Ii et al 46 and Di Gregorio et al 47 , partitioning of a territory into subdomains was proposed so that the CCO algorithm could independently be applied to vascularise non-overlapping subdomains. Meanwhile, Jaquet et al 43 , 66 proposed to solve concurrency by assigning a relative flow quota for each input, while those who temporally exceed their quota are put on hold.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the use of fractal networks to assess closure boundary conditions in hemodynamic simulations 70 , 71 , or the use of CCO networks to predict the arteriolar pressure in the brain 72 . More detailed generated networks have been proposed for heart 43 , 47 , 66 and brain 46 , 67 following a methodology specific for the organ of interest.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive constrained constructive optimisation for complex vascularisation processes

Talou

Safaei

Hunter

et al. 2021

Sci Rep

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Mimicking angiogenetic processes in vascular territories acquires importance in the analysis of the multi-scale circulatory cascade and the coupling between blood flow and cell function. The present work extends, in several aspects, the Constrained Constructive Optimisation (CCO) algorithm to tackle complex automatic vascularisation tasks. The main extensions are based on the integration of adaptive optimisation criteria and multi-staged space-filling strategies which enhance the modelling capabilities of CCO for specific vascular architectures. Moreover, this vascular outgrowth can be performed either from scratch or from an existing network of vessels. Hence, the vascular territory is defined as a partition of vascular, avascular and carriage domains (the last one contains vessels but not terminals) allowing one to model complex vascular domains. In turn, the multi-staged space-filling approach allows one to delineate a sequence of biologically-inspired stages during the vascularisation process by exploiting different constraints, optimisation strategies and domain partitions stage by stage, improving the consistency with the architectural hierarchy observed in anatomical structures. With these features, the aDaptive CCO (DCCO) algorithm proposed here aims at improving the modelled network anatomy. The capabilities of the DCCO algorithm are assessed with a number of anatomically realistic scenarios.

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A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue

Fritz

Köppl

Oden

et al. 2022

Numer Methods Biomed Eng

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In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D-0D-3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D-0D-3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.

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A computational model applied to myocardial perfusion in the human heart: From large coronaries to microvasculature

Cited by 33 publications

References 78 publications

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Adaptive constrained constructive optimisation for complex vascularisation processes

A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue

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