Using computed tomography angiography and computational fluid dynamics to study aortic coarctation in different arch morphologies

Qin, Jinjie; Ouyang, Da; Yan, Taocui; Wang, Haoru; Guo, Ke; Jin, Xin; Pan, Zhengxia; He, Ling

doi:10.3389/fped.2023.1131025

Cited by 1 publication

(1 citation statement)

References 38 publications

(47 reference statements)

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“…Unsteady flow simulations of the aortic flow are associated with high computational costs, which are, based on our experience, approximately 10-fold compared with a peak-systolic flow simulation. This is because at least two heart cycles have to be simulated to achieve a time-independent solution and because usual time steps between 0.0004 s and 0.005 s ( Qin et al, 2023 ) used for aortic flow simulations result in approximately a few hundred or thousands of time steps to be simulated. Unsteady flow simulations are necessary to assess velocity and pressure fields accurately, especially during high flow acceleration and deceleration phases.…”

Section: Discussionmentioning

confidence: 99%

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks

Versnjak,

Yevtushenko,

Kuehne

et al. 2024

Front. Physiol.

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The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-of-concept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23 mmHg) outperforms the 1D BRNN (median RMSE of 4.25 mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03 mmHg) compared to the 3D CNN (±8.91 mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model’s training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model’s accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic versus non-gothic) does not improve ANN performance for test cases sharing the same shape.

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Section: Discussionmentioning

confidence: 99%