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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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