Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms.

We describe and evaluate methods that create detailed vessel trees by linking vessels that have been segmented from magnetic resonance angiograms (MRA). The tree-definition process can automatically exclude erroneous vessel segmentations. The parent-child connectivity information provided by our vessel trees is important to both surgical planning and to guidance of endovascular procedures. We evaluated the branch connection accuracy of our 3D vessel trees by asking two neuroradiologists to evaluate 140 parent-child connections comprising seven vascular trees against 17 digital subtraction angiography (DSA) views. Each reviewer rated each connection as (1) Correct, (2) Incorrect, (3) Partially correct (a minor error without clinical significance), or (4) Indeterminate. Analysis was summarized for each evaluator by calculating 95% confidence intervals for both the proportion completely correct and the proportion clinically acceptable (completely or partially correct). In order to protect the overall Type I error rate, alpha-splitting was done using a top down strategy. We additionally evaluated segmentation completeness by examining each slice in 11 MRA datasets in order to determine unlabeled vessels identifiable in cross-section following segmentation. Results indicate that only one vascular parent-child connection was judged incorrect by both reviewers. MRA segmentations appeared complete within MRA resolution limits. We conclude that our methods permit creation of detailed vascular trees from segmented 3D image data. We review the literature and compare other approaches to our own. We provide examples of clinically useful visualizations enabled by our methodology and taken from a visualization program now in clinical use.