Abstract #3378

Performance of Automatic Cerebral Arterial Segmentation of MRA Images Improves at Ultra-high Field.

Alexander Saunders^1,2, Tales Santini³, Tiago Martins³, Howard Aizenstein³, John C. Wood¹, Matthew Borzage (co-corresponding author)¹, and Tamer S. Ibrahim (co-corresponding author)³

¹Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States, ²Rudi Schulte Research Institute, Santa Barbara, CA, United States, ³Swanson School of Engineering and School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States

Ultra-high field time of flight MRA can generate images with higher signal and resolution but image quality may suffer from increased field inhomogeneity. Because we would like to extract arteries for further analysis, we sought to evaluate automatic segmentation performance compared with standard field strength. Five segmentation algorithms were applied to two MRA images (one 3T, one 7T) and performance was measured against manually segmented ground truth data. We found that automatic segmentation performs better in 7T images but confounds in acquisition and image processing need to be further investigated.

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