Abstract: We propose a model for the interactive interpretation of medical images pertaining to human neurovascular system. This attentionally-based interactive model, AIM, is founded upon human selective attention. AIM combines human operator's high level reasoning with machine perception and exploits human interaction as part of the solution. AIM defines two channels of interaction: context ("what to look for"), and focus-of-attention ("where to look") by which the user directs the attention of the machine perception. AIM facilitates varying degrees of human intervention in the process by providing four levels of abstraction for the context information. This hierarchy of context abstractions permits the system to function more autonomously (doing high-level tasks like extracting an arterial vessel) in routine interpretation, and to require more user intervention (e.g. locating arterial wall boundaries) as the image complexity increases or data quality worsen. This is important in medical imaging where the users demand ultimate control and confidence in the system. Such technology can contribute significantly on the design of radiological imaging systems.