Combining MRI blood flow data with one-dimensional blood flow models to perform patient-speciffic noninvasive pressure prediction

Abstract

In this thesis, the behaviour and application of a one-dimensional model describing blood flow through compliant vessels is investigated. The one-dimensional model is derived using the physical laws of conservation of momentum and conservation of mass and uses a Finite Volume Method in combination with a high resolution flux difference splitting. First, the behaviour of the model is compared with established models in a network of 55 main arteries and then in a network of 111 main arteries including in vivo results. Finally, the model with the 111-artery network as a baseline is combined with inflow boundary conditions and a scaling factor provided by MRI blood flow data
to make it patient-speciffic. The adapted model is used to investigate how well characteristics in the distal part of the network can be predicted. It is found that the Finite Volume Method in combination with the high resolution flux difference splitting produces results that are highly comparable to those of established models both in the 55- and 111-artery network. The patient-speciffic model proves to be capable of predicting the pressure value in the arm to a reasonable degree. The flow in distal parts in the aorta is underestimated for all patients, but the degree of this underestimation varies per patient. Combining the results with a novel colour representation of pressure and velocity, it is possible to show patient speciffic evolution of pressure and velocity in their arteries. Further research should focus on adapting baseline networks with different key characteristics to increase the accuracy of the predictions for patients with different characteristics than the baseline model used in this study. Such characteristics include the number of bifurcations in the aortic arch (three or two) and the tapering of the aorta.