  During the diagnosis of ischemic strokes, the Circle of Willis and its
surrounding vessels are the arteries of interest. Their visualization in case
of an acute stroke is often enabled by Computed Tomography Angiography (CTA).
Still, the identification and analysis of the cerebral arteries remain time
consuming in such scans due to a large number of peripheral vessels which may
disturb the visual impression. In previous work we proposed VirtualDSA++, an
algorithm designed to segment and label the cerebrovascular tree on CTA scans.
Especially with stroke patients, labeling is a delicate procedure, as in the
worst case whole hemispheres may not be present due to impeded perfusion.
Hence, we extended the labeling mechanism for the cerebral arteries to identify
occluded vessels. In the work at hand, we place the algorithm in a clinical
context by evaluating the labeling and occlusion detection on stroke patients,
where we have achieved labeling sensitivities comparable to other works between
92\,\% and 95\,\%. To the best of our knowledge, ours is the first work to
address labeling and occlusion detection at once, whereby a sensitivity of
67\,\% and a specificity of 81\,\% were obtained for the latter. VirtualDSA++
also automatically segments and models the intracranial system, which we
further used in a deep learning driven follow up work. We present the generic
concept of iterative systematic search for pathways on all nodes of said model,
which enables new interactive features. Exemplary, we derive in detail,
firstly, the interactive planning of vascular interventions like the mechanical
thrombectomy and secondly, the interactive suppression of vessel structures
that are not of interest in diagnosing strokes (like veins). We discuss both
features as well as further possibilities emerging from the proposed concept.
