Abstract #2719
One step toward automating vessel detection and labeling in the neck for flow quantification
Ying Wang 1,2 , Jing Jiang 1,3 , Paul Kokeny 1 , Yi Zhong 4 , and E. Mark Haacke 1,4
1
Department of Biomedical Engineering, Wayne
State University, Detroit, MI, United States,
2
College
of Information Science and Engineering, Northeastern
University, Shenyang, Liaoning, China,
3
Department
of Radiology, Wayne State University, Detroit, MI,
United States,
4
MR
Innovations, Inc., Detroit, MI, United States
Quantifying flow from 2D phase contrast MRI data
requires that the vessels of interest be identified and
segmented. Doing so manually is time consuming and
depends on the skill level of the processor. Here, a
tissue similarity mapping (TSM) based automatic
segmentation and labeling method for use in the neck is
proposed. Magnitude and phase information is utilized
through TSM to extract and classify vessels as arteries
or veins. A priori knowledge about vessel locations are
used to identify ten major vessels found at the C6
level. Accuracy of the method is demonstrated on in vivo
human data.
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