Visualization: Semi-Automatic Visualization and Quantification of Intra-Cardiac 4D Flow MRI Data for Large Patient Studies
Besides the long acquisition times, application of 4D flow is hindered by the complexity of the analysis of the comprehensive data sets. Over the past decades, many approaches for visualization of intracardiac blood flow have been published. As the complete time-resolved three-directional three-dimensional velocity field is measured, the blood flow can be analyzed using a large range of tools. Visualization can be performed using cut planes, isosurfaces, vector plots and particle traces, as examples. Particle traces, i.e. streamlines and pathlines, have become especially popular for intuitive visualization of time-resolved 3D blood flow. Quantitative measures as volume flow can also be obtained using 4D MR with off-line retrospective valve tracking. Analysis of 4D flow data is extremely time consuming, especially during the heart segmentation stage. In spite of this limitation, some approaches have used the information available in the acquisitions to analyze values like flow components, kinetic energy, linear momentum and early vs. late diastolic inflow.
Analysis of cardiac images requires often segmentation of the heart’s chambers and large vessels, which presents a problem in velocity MRIs, since there is no good contrast between myocardium and blood. Some studies have combined velocity information with the intensity gradients in the MRI in order to obtain a more stable segmentation of the heart’s blood vessels. More advanced methods are available for automatic left ventricle segmentation using techniques such as deformable models, active shape and appearance models, atlas-guided registration, among others. These techniques have reduced the processing time needed to analyze magnitude MRI images, but they still need to be improved in order to obtain results equal to manual delineation and probably must be modified in order to be useful on velocity MRI analysis.
This project aims to obtain automatic quantification and visualization of cardiac MRI data, and specifically blood flow data, using atlas-based segmentation of the whole heart.