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Patient-specific and real-time model of numerical simulation of the hemodynamics of type B aortic dissections

Abstract : INTRODUCTION: Regular monitoring of uncomplicated type B aortic dissection is essential because 25-30% will progress to aneurysmal form. The predictive factors of this evolution are not clearly defined, but they seem to be correlated with hemodynamic data. HYPOTHESIS: Our goal is to create a patient-specific and real-time model of numerical simulation of the hemodynamics of uncomplicated type B aortic dissections in order to predict the evolution of these pathologies for earlier treatment. METHOD: This model consists in a coupling 0D (hydraulic-electric analogy) - 3D (CT angiography segmentation) of the aortic arch with optimization by comparison to the 2D Phase Contrast MRI data and using Reduced Order Models to drastically reduce computing times. We tested our model on a healthy and a dissected patient. Then we realized different systolic blood pressure scenarios for each case, which we compared. RESULTS: In the dissected patient, the blood pressure at the false lumen wall was less important than the true lumen. Furthermore, the aortic wall shear stress and the velocity fields in aorta increase at the entry and re-entry tears between the two lumens. The simulation of different blood pressures scenarios shows a decrease in all these three parameters related to the decrease of the systolic blood pressure. CONCLUSION: Our model provides reliable patient-specific and real-time 3D rendering. It has also allowed us to realize different flow variation scenarios to simulate different clinical conditions and to compare them. However, the model still needs improvement in view of a daily clinical application.
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Submitted on : Wednesday, February 12, 2020 - 1:33:36 PM
Last modification on : Thursday, February 13, 2020 - 1:09:45 AM
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J Tomasi, F Le Bars, C Shao, A Lucas, M Lederlin, et al.. Patient-specific and real-time model of numerical simulation of the hemodynamics of type B aortic dissections. Medical Hypotheses, Elsevier, 2020, 135, pp.109477. ⟨10.1016/j.mehy.2019.109477⟩. ⟨hal-02393818⟩

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