A novel MRI-based data fusion methodology for efficient, personalised, compliant simulations of aortic haemodynamics

Stokes, Catriona; Bonfanti, Mirko; Li, Zeyan; Xiong, Jiang; Chen, Duanduan; Balabani, Stavroula; Díaz‐Zuccarini, Vanessa

doi:10.1016/j.jbiomech.2021.110793

Cited by 25 publications

(21 citation statements)

References 46 publications

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“…On average, the TAWSS of the compliant-wall simulation has been found to be 21.5% lower than that of a rigid-wall simulation for a hemodialysis arteriovenous fistula ( McGah et al, 2014 ). Other studies reported a similar magnitude of difference (to be 25%) in WSS in an idealized carotid bifurcation model ( Perktold and Rappitsch, 1995 ) or smaller (13%) in aortas reconstructed from MR images ( Stokes et al, 2021 ). Furthermore, the effects of assuming a rigid wall on hemodynamic factors may not be uniform across different regions.…”

Section: Verification Validation and Uncertainty Quantificationmentioning

confidence: 66%

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Northrup

et al. 2022

Front. Bioeng. Biotechnol.

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Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

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Section: Verification Validation and Uncertainty Quantificationmentioning

confidence: 66%

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Northrup

et al. 2022

Front. Bioeng. Biotechnol.

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“…where i denotes the mode number. 3 To perform the decomposition (Equation 4), the timeaveraged velocity u(x, y) is first subtracted from each instantaneous velocity field, obtaining a set of m fluctuating 240 velocity fields u ′ (x, y, t). The dataset is then rearranged in a 2n × m snapshot matrix U:…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

“…The ability to visualize and investigate the flow features inside the aorta, either by in vitro experiments or Computational Fluid Dynamics (CFD), can provide invaluable information for clinical support, disease progression predictions and surgical treatment planning [1]. Application of such tools has been successfully demonstrated in several pathologies, such as aortic dissection [1, 2, 3], coronary artery disease [4], valve prosthesis [5], aortic aneurysm [6] and congenital heart disease [7]. Both in vitro and in silico hemodynamic approaches are subject to certain limitations that impact their clinical translation.…”

Section: Introductionmentioning

confidence: 99%

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to Capture Personalised Aortic Haemodynamics

Chatpattanasiri

Franzetti

Bonfanti

et al. 2023

Preprint

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Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental hemodynamic models. In this work, we explore the use of Reduced Order Models (ROMs) to capture the main flow features in a patient-specific dissected aorta. We apply Proper Orthogonal Decomposition (POD) and Robust Principle Component Analysis (RPCA) on in vitro, hemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). RPCA has been found to enhance the quality of PIV data and to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes phi 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations that can pave the way for translation of such models to the clinic.

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“…By combining 4D-Flow MRI and CFD techniques it is possible to run patient-specific simulations that have the potential to aid treatment planning, disease progression and further the understanding of the haemodynamics due to its predictive capabilities 19 – 21 . Patient-specific CFD simulations employ geometry and boundary conditions that are determined from 4D-Flow MRI data.…”

Section: Introductionmentioning

confidence: 99%

The impact of 4D-Flow MRI spatial resolution on patient-specific CFD simulations of the thoracic aorta

Cherry

Khatir

Khan

et al. 2022

Sci Rep

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Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging technologies as it allows for accurate imaging of blood vessels. 4-Dimensional Flow Magnetic Resonance Imaging (4D-Flow MRI) is built on conventional MRI, and provides flow data in the three vector directions and a time resolved magnitude data set. As such it can be used to retrospectively calculate haemodynamic parameters of interest, such as Wall Shear Stress (WSS). However, multiple studies have indicated that a significant limitation of the imaging technique is the spatiotemporal resolution that is currently available. Recent advances have proposed and successfully integrated 4D-Flow MRI imaging techniques with Computational Fluid Dynamics (CFD) to produce patient-specific simulations that have the potential to aid in treatments,surgical decision making, and risk stratification. However, the consequences of using insufficient 4D-Flow MRI spatial resolutions on any patient-specific CFD simulations is currently unclear, despite being a recognised limitation. The research presented in this study aims to quantify the inaccuracies in patient-specific 4D-Flow MRI based CFD simulations that can be attributed to insufficient spatial resolutions when acquiring 4D-Flow MRI data. For this research, a patient has undergone four 4D-Flow MRI scans acquired at various isotropic spatial resolutions and patient-specific CFD simulations have subsequently been run using geometry and velocity data produced from each scan. It was found that compared to CFD simulations based on a $$1.5\,{\text {mm}} \times 1.5\,{\text {mm}} \times 1.5\,{\text {mm}}$$ 1.5 mm × 1.5 mm × 1.5 mm , using a spatial resolution of $$4\,{\text {mm}} \times 4\,{\text {mm}} \times 4\,{\text {mm}}$$ 4 mm × 4 mm × 4 mm substantially underestimated the maximum velocity magnitude at peak systole by $$110.55\%$$ 110.55 % . The impacts of 4D-Flow MRI spatial resolution on WSS calculated from CFD simulations have been investigated and it has been shown that WSS is underestimated in CFD simulations that are based on a coarse 4D-Flow MRI spatial resolution. The authors have concluded that a minimum 4D-Flow MRI spatial resolution of $$1.5\,{\text {mm}} \times 1.5\,{\text {mm}} \times 1.5\,{\text {mm}}$$ 1.5 mm × 1.5 mm × 1.5 mm must be used when acquiring 4D-Flow MRI data to perform patient-specific CFD simulations. A coarser spatial resolution will produce substantial differences within the flow field and geometry.

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A novel MRI-based data fusion methodology for efficient, personalised, compliant simulations of aortic haemodynamics

Cited by 25 publications

References 46 publications

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to Capture Personalised Aortic Haemodynamics

The impact of 4D-Flow MRI spatial resolution on patient-specific CFD simulations of the thoracic aorta

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