A hybrid <scp>echocardiography‐CFD</scp> framework for ventricular flow simulations

Hedayat, Mohammadali; Patel, Tatsat R.; Kim, Taeouk; Bělohlávek, Marek; Hoffmann, Kenneth R.; Borazjani, Iman

doi:10.1002/cnm.3352

Cited by 9 publications

(6 citation statements)

References 75 publications

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“…Regarding discrepancies between TTE measurements and simulations, higher differences occurred especially during peak systole and late diastole for AV and MV velocities, respectively. Hedayat et al reconstructed the LV geometry movement from 2D echocardiography images and compared simulated MV velocities during diastole with Doppler data [19]. In this study, peak velocities were slightly higher within simulation than within Doppler measurements.…”

Section: Discussionmentioning

confidence: 71%

“…For the flow quantification via TTE, focus was set on the valve velocities via PW-Doppler, as commonly done in verification and validation studies in the field [6,19,31]. The velocities were obtained by manually reconstructing the envelope curve of the PW-Doppler measurements (see Figure 3B).…”

Section: Flow Quantification Based On Tte Datamentioning

confidence: 99%

“…Looking at TTE, no risks of harming the individual exist. Taking 2D echocardiography images as a basis, Hedayat et al [19] developed a patient‐specific 3D wall geometry and movement reconstruction method and applied it on pig data. Larsson et al [31] made use of the comparably easy acquisition of TTE data and validated their CFD computations against Doppler acquisitions for 100 subjects.…”

Section: Introductionmentioning

confidence: 99%

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Inter‐model and inter‐modality analysis of left ventricular hemodynamics: Comparative study of two CFD approaches based on echocardiography and magnetic resonance imaging

Obermeier,

Korte,

Vellguth

et al. 2024

GAMM-Mitteilungen

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Computational fluid dynamics (CFD) carry the potential to provide detailed insights into intraventricular hemodynamics and complement in vivo flow measurement techniques. A variety of CFD approaches emerged in recent years, mostly building solely on medical image data as patient‐specific input. While the utilized medical imaging method and chosen CFD approach both influence the computed hemodynamics, thereto related differences are rarely investigated. The present study addresses this issue with an inter‐(imaging)‐modality and inter‐model comparison of intracardiac flow computations. Magnetic resonance imaging (MRI) and transthoracic echocardiography (TTE) data of a volunteer were acquired and used to reconstruct the anatomical structures. For each modality, the reconstructed shapes were applied in two previously introduced CFD approaches to compute whole‐cycle ventricular flow patterns. While both methods involved benefits and challenges, similar valve velocities were computed, being in accordance with in vivo 4D flow MRI and pulsed‐wave Doppler velocity measurements (systolic peak velocity: 1.24–1.26 m/s (MRI), 0.9–1.25 m/s (TTE); diastolic peak velocity: 0.54 m/s (MRI), 0.59–0.75 m/s (TTE)). A detailed flow analysis with vortex formation, kinetic energy, and mid‐ventricular velocities indicated the computed inter‐modality differences to be larger than inter‐method ones. Quantitatively, this could be observed in the direct flow rate ( inter‐modality: 13, inter‐method, 3). These results help to gain trust in CFD approaches to compute intraventricular flow and emphasize the importance of standardized input data. Future studies, however, should consider a broader data base.

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Section: Discussionmentioning

confidence: 71%

Section: Flow Quantification Based On Tte Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inter‐model and inter‐modality analysis of left ventricular hemodynamics: Comparative study of two CFD approaches based on echocardiography and magnetic resonance imaging

Obermeier,

Korte,

Vellguth

et al. 2024

GAMM-Mitteilungen

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“…It is now well understood that the simple design of BMHV induces non-physiologic flow patterns to form at the aortic root due to the opening and closing of the two leaflets [1,21]. Therefore, understanding the hemodynamic environment induced by the prosthetic valve is critical to improve the valve design and implantation procedure [42][43][44][45].…”

Section: Mechanical Valvesmentioning

confidence: 99%

Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies

Usta

Aidun

et al. 2022

Fluids

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Given the complexity of human left heart anatomy and valvular structures, the fluid–structure interaction (FSI) simulation of native and prosthetic valves poses a significant challenge for numerical methods. In this review, recent numerical advancements for both fluid and structural solvers for heart valves in patient-specific left hearts are systematically considered, emphasizing the numerical treatments of blood flow and valve surfaces, which are the most critical aspects for accurate simulations. Numerical methods for hemodynamics are considered under both the continuum and discrete (particle) approaches. The numerical treatments for the structural dynamics of aortic/mitral valves and FSI coupling methods between the solid Ωs and fluid domain Ωf are also reviewed. Future work toward more advanced patient-specific simulations is also discussed, including the fusion of high-fidelity simulation within vivo measurements and physics-based digital twining based on data analytics and machine learning techniques.

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“…For the future work, the automatic delineation method needs to be extended to segment the valves, which is not a trivial task because only small numbers of pixels represent the valves in standard 2D echo projections thus making it hard to distinguish them from LV ends in long-axis and tendineae in short-axis projections. Nevertheless, such capability enables creating a closed geometry that can be used for image-based computational fluid dynamics (CFD) simulations (47,48) and fluid-structure interaction simulations (49).…”

Section: Future Workmentioning

confidence: 99%

Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks

Kim¹,

Hedayat²,

Vaitkus³

et al. 2021

Quant Imaging Med Surg

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Background: Two-dimensional echocardiography (2D echo) is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Boundary identification of left ventricle (LV) in 2D echo, i.e., image segmentation, is the first step to calculate relevant clinical parameters. Currently, LV segmentation in 2D echo is primarily conducted semi-manually. A fully-automatic segmentation of the LV wall needs further development.Methods: We evaluated the performance of the state-of-the-art convolutional neural networks (CNNs) for the segmentation of 2D echo images from 6 standard projections of the LV. We used two segmentation algorithms: U-net and segAN. The models were trained using an in-house dataset, which consists of 1,649 porcine images from 6 to 8 different pigs. In addition, a transfer learning approach was used for the segmentation of long-axis projections by training models with our database based on the previously trained weights obtained from Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset.The models were tested on a separate set of images from two other pigs by computing several metrics. The segmentation process was combined with a 3D reconstruction framework to quantify the physiological indices such as LV volumes and ejection fraction (EF). Results:The average dice metric for the LV cavity was 0.90 and 0.91 for the U-net and segAN, respectively, which was higher than 0.82 for the level-set (P value: 3.31×10 −25 ). The average Hausdorff distance for the LV cavity was 2.71 mm and 2.82 mm for the U-net and segAN, respectively, which was lower than 3.64 mm for the level-set (P value: 4.86×10 −16 ). The LV shapes and volumes obtained using the CNN segmentation models were in good agreement with the results segmented by the experts. In addition, the differences of the calculated physiological parameters between two 3D reconstruction models segmented by the experts and CNNs were less than 15%. Conclusions:The results showed that both CNN models achieve higher performance on LV segmentation than the level-set method. The error of the reconstruction from automatic segmentation compared to the expert segmentation is less than 15%, which is within the 20% error of echo compared to the gold standard.

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A hybrid echocardiography‐CFD framework for ventricular flow simulations

Cited by 9 publications

References 75 publications

Inter‐model and inter‐modality analysis of left ventricular hemodynamics: Comparative study of two CFD approaches based on echocardiography and magnetic resonance imaging

Inter‐model and inter‐modality analysis of left ventricular hemodynamics: Comparative study of two CFD approaches based on echocardiography and magnetic resonance imaging

Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies

Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks

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