On the Periodicity of Cardiovascular Fluid Dynamics Simulations

Pfaller, Martin R.; Pham, Jonathan; Wilson, Nathan M.; Parker, David; Marsden, Alison L.

doi:10.1007/s10439-021-02796-x

Cited by 23 publications

(21 citation statements)

References 43 publications

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“…This process can easily consume several days, even while using multiple processors and high-performance computing clusters. As such, we use a novel initialization method to initialize our 3D simulations and minimize the number of cardiac cycles required for each patient-specific model considered in this work [51]. All 3D simulations were run on Stanford's Sherlock supercomputing cluster using four 12-core Intel Xeon Gold 5118 CPUs.…”

Section: D Modelingmentioning

confidence: 99%

“…All 3D simulations were run on Stanford's Sherlock supercomputing cluster using four 12-core Intel Xeon Gold 5118 CPUs. Using our 0D validation method, we ensured that the pressure error to the periodic state in the 3D solutions is below 1 % at all outlets [51].…”

Section: D Modelingmentioning

confidence: 99%

“…Generating the centerlines for all N = 73 models took ∼ 6 h. The centerlines need to be extraced only once per geometry, the rest of the 0D and 1D model generation pipeline is instantaneous. We estimated the number of cardiac cycles required to reach a periodic state from initial conditions generated from a mean flow solution based on the results in a prior study [51]. It took about ∼ 2 h to run all 0D models and about ∼ 5 h to run all 1D models.…”

Section: Computational Performancementioning

confidence: 99%

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Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Pfaller,

Pham,

Verma

et al. 2021

Preprint

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Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computer. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood flow and pressure with only a fraction of the cost. They can accelerate uncertainty quantification, optimization, and design parameterization studies. Previously, these models needed to be created laboriously by hand, and this limited assessment of their approximation accuracy to very few models in prior studies. This work proposes a fully automated and openly available framework to generate and simulate 0D and 1D models from 3D patient-specific geometries. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against full 3D solutions in a comprehensive comparison with N = 73 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1 % to 10 % for both 0D and 1D models, at the caps and inside the vessel branches. Though they have minimally higher approximation errors than 1D, we recommend using 0D models due to their robustness and computational efficiency. Automatically generated reduced-order models can significantly speed up model development and shift the computational load from high-performance to personal computers.

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Section: D Modelingmentioning

confidence: 99%

Section: D Modelingmentioning

confidence: 99%

Section: Computational Performancementioning

confidence: 99%

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Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Pfaller,

Pham,

Verma

et al. 2021

Preprint

Self Cite

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“…Yet, three-dimensional (3D) methods typically require several hours of parallel computing. 1 Runtime is a severe limitation since applications of patient-specific modeling often require not only a single simulation, but also numerous simulations. For example, iterative parameter estimation is often performed to match clinical targets, for example, from in vivo magnetic resonance flow imaging and catheter pressure measurements.…”

Section: Introductionmentioning

confidence: 99%

Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow

Pfaller

Pham

Verma

et al. 2022

Numer Methods Biomed Eng

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Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster.One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the opensource software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.

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“…12 In order for in silico medicine to become a reality, the methods developed to solve the model equations need to be efficient. For example, Pfaller et al 11 proposed a methodology to monitor and achieve faster blood flow periodicity of the solution using a coarser scale initial solution. Identifying what model refinement is needed depending on the desired output is also important for the clinical application, as demonstrated for the case of brain perfusion by Josza et al 8 A key component of the application to the clinical setting is model parameterization from patient data and the associated uncertainty quantification.…”

mentioning

confidence: 99%

Special Issue of the VPH2020 Conference: “Virtual Physiological Human: When Models, Methods and Experiments Meet the Clinic”

et al. 2022

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On the Periodicity of Cardiovascular Fluid Dynamics Simulations

Cited by 23 publications

References 43 publications

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow

Special Issue of the VPH2020 Conference: “Virtual Physiological Human: When Models, Methods and Experiments Meet the Clinic”

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