Quantitative Hemodynamics using Magnetic Resonance Imaging, Computational Fluid Dynamics and Physics-informed Neural Network

by Dandan Ma
Doctoral thesis
Date of Examination:2023-05-03
Date of issue:2023-06-20
Advisor:Prof. Dr. Martin Uecker
Referee:Prof. Dr. Martin Uecker
Referee:Prof. Dr. Stefan Klumpp
Persistent Address: http://dx.doi.org/10.53846/goediss-9939

 

 

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Abstract

English

Hemodynamics is related to blood flows in the circulatory system under normal and pathological conditions to understand the mechanisms of various diseases and to treat them accordingly. Different methods, with their own advantage and weaknesses, have been adopted for hemodynamic studies. In this dissertation, two new strategies are developed by combining different methods, such as Magnetic Resonance Imaging (MRI), Computational Fluid Dynamics (CFD), and Physics-informed Neural Network (PINN) based on physical information and applied them for the quantitative study of hemodynamics. The strategies take full advantage of each method, allowing better quantification of hemodynamics and better prediction of the outcome of surgical protocols. Specifically, the first strategy, combining MRI and CFD, for personalized stent intervention in aortic coarctation was proposed. To validate the accuracy of CFD, I first performed numerical simulations using different turbulence modeling methods and compared the numerical results with experimental data from flow MRI. The validated CFD method is further used to study the blood flows within virtually deformed aortic geometries, and to guide stent implantation. Although only the aorta was considered, such a strategy can be extended to other stenosis, and can be used to help clinicians to evaluate surgical plans before intervention. The second strategy was proposed to accomplish blood flow prediction by combing MRI and PINN. Such a strategy overcomes the weaknesses of MRI and CFD for hemodynamics. Prediction of both laminar and turbulent blood flows with PINN in 3D-printed idealized and realistic aortic geometries was performed. Flow data and geometric information from the MRI were considered as inputs for the training. The obtained results show that image-based PINN can overcome the weaknesses of MRI and CFD, which are currently widely used for hemodynamic studies and can predict flow quantitatively using small amounts of noisy data. In summary, in this dissertation I have developed two new strategies by combining different methods for quantitative study of hemodynamics. Bringing together physics, medicine, and computer science, these strategies may shed light on precision medicine and personalized medical therapy.
Keywords: aorta; stent intervention; large eddy simulation; direct numerical simulation; magnetic resonance imaging; physics-informed Neural Network
 
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