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Background. Magnetic resonance imaging (MRI) using three-dimensional velocity encoding phase contrast (PC) methods offers the opportunity to quantify time-resolved 3D flow patterns in vivo . This technique can have a breakthrough impact on the evaluation, risk stratification and surgical planning in hemodynamic-related pathologies, e.g., cardiac valve diseases, arterial stenos or insufficiency, aortic dilation, dissection or coartaction. However, its applicability in clinics is limited due to the complex post-processing required to extract the information and the difficulty to synthesize the obtained data into clinical useful parameters. Objective. In this work, a software tool is presented which analyzes the row data and provides information along the whole vessel, between two selected cross-sections and in the vicinity of the selected points. Methods. A fully automatic algorithm based on the properties of the steady Hagen–Poiseuille flow was developed which in few minutes segments the vessel shape, visualize the blood flow and calculates its characteristics. Since the time and space resolutions of the data are limited, we avoid the differentiation of the velocity field. Results. The algorithm has been tested on datasets of patients with bicuspid aortic valve and healthy volunteers. Results are provided both as maximum and time-averaged values in aorta, pulmonary artery, left and right ventricles. Conclusions. The results demonstrate that the presented approach could be useful for medical doctors in order to classify and stratify different valve and/or vessel pathologies.

Global and Local Characteristics of the Blood Flow in Large Vessels Based on 4D MRI Data