Name
Abstract | ||
|---|---|---|
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) is a method to non-invasively acquire in-vivo blood flow, e.g. in the aorta. It produces three-dimensional, time-resolved datasets containing both flow speed and direction for each voxel. In order to perform qualitative and quantitative data analysis on these datasets, a vessel segmentation is often required. These segmentations are mostly performed manually or semi-automatically, based on three-dimensional intensity images containing the maximal flow speed over all time steps. To allow for a faster segmentation, we propose a method that, in addition to intensity, incorporates the flow trajectories into the segmentation process. This is accomplished by extracting Lagrangian Coherent Structures (LCS) from the flow data, which indicate physical boundaries in a dynamical system. To approximate LCS in our discrete images, we employ Finite Time Lyapunov Exponent (FTLE) fields to quantify the rate of separation of neighboring flow trajectories. LCS appear as ridges or valleys in FTLE images, indicating the presence of either a flow structure boundary or physical boundary. We will show that the process of segmenting low-contrast 4D PC-MRI datasets can be simplified by using the generated FLTE data in combination with intensity images. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.2312/vcbm.20161269 | VCBM |
Field | DocType | ISBN |
Voxel,Computer vision,Pattern recognition,Segmentation,Flow (psychology),Filter (signal processing),Boundary detection,Artificial intelligence,Flow velocity,Lyapunov exponent,Dynamical system,Mathematics | Conference | 978-3-03868-010-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Behrendt | 1 | 0 | 0.34 |
| Benjamin Köhler | 2 | 40 | 8.17 |
| D. Gräfe | 3 | 0 | 0.34 |
| Matthias Grothoff | 4 | 5 | 2.81 |
| Matthias Gutberlet | 5 | 42 | 4.60 |
| Bernhard Preim | 6 | 1766 | 235.86 |