No abstract

The knowledge of cerebrovascular variants is essential in education, training, diagnosis and treatment. The current way of presentation of vasculature and, particularly, vascular variants is insufficient. Our purpose is to construct a three-dimensional (3D) interactive atlas of cerebral arterial variants along with exploration tools allowing the investigator just with a few clicks to better and faster understand the variants and their spatial relationships. A 3D model of the cerebral arterial system created earlier, fully labeled with names and diameters, is used as a reference. As the vast material about vascular variability is incomplete and not fully documented, our approach synthesizes variants in 3D based on existing knowledge. The variants are created from literature using a dedicated vascular editor and embedded into the reference model. Sixty 3D variants and branching patterns are created including the internal carotid, middle cerebral, anterior cerebral, posterior cerebral, vertebral and basilar arteries, and circle of Willis. Their prevalence rates are given. The atlas is developed to explore the variants individually or embedded into the reference vasculature. Real-time interactive manipulation of variants and reference vasculature (rotate/zoom/pan/view) is provided. This atlas facilitates the investigator to easily get familiarized with the variants and rapidly explore them. It aids in presentation of vascular variants and understanding their spatial relationships either individually or embedded into the surrounding reference cerebrovasculature. It is useful for medical students, educators to prepare teaching materials, and clinicians for scan interpretation. It is easily extensible with additional variant instances, new variants, branching patterns, and supporting textual materials.

Preparation of tests and student's assessment by the instructor are time consuming. We address these two tasks in neuroanatomy education by employing a digital media application with a three-dimensional (3D), interactive, fully segmented, and labeled brain atlas. The anatomical and vascular models in the atlas are linked to Terminologia Anatomica. Because the cerebral models are fully segmented and labeled, our approach enables automatic and random atlas-derived generation of questions to test location and naming of cerebral structures. This is done in four steps: test individualization by the instructor, test taking by the students at their convenience, automatic student assessment by the application, and communication of the individual assessment to the instructor. A computer-based application with an interactive 3D atlas and a preliminary mobile-based application were developed to realize this approach. The application works in two test modes: instructor and student. In the instructor mode, the instructor customizes the test by setting the scope of testing and student performance criteria, which takes a few seconds. In the student mode, the student is tested and automatically assessed. Self-testing is also feasible at any time and pace. Our approach is automatic both with respect to test generation and student assessment. It is also objective, rapid, and customizable. We believe that this approach is novel from computer-based, mobile-based, and atlas-assisted standpoints.

The human cerebrovasculature is extremely complicated and its three dimensional (3D) highly parcellated models, though necessary, are unavailable. We constructed a digital cerebrovascular model from a high resolution, 3T 3D time-of-flight magnetic resonance angiography scan. This model contains the arterial and venous systems and is 3D, geometric, highly parcellated, fully segmented, and completely labeled with name, diameter, and variants. Our approach replaces the tedious and time consuming process of checking and correcting automatic segmentation results done at 2D image level with an aggregate and faster process at 3D model level. The creation of the vascular model required vessel pre-segmentation, centerline extraction, vascular segments connection, centerline smoothing, vessel surface construction, vessel grouping, tracking, editing, labeling, setting diameter, and checking correctness and completeness. For comparison, the same scan was segmented automatically with 59.8% sensitivity and only 16.5% of vessels smaller than 1 pixel size were extracted. To check and correct this automatic segmentation requires 8 weeks. Conversely, the speedup of our approach (the number of 2D segmented areas/the number of 3D vascular segments) is 34. This cerebrovascular model can serve as a reference framework in clinical, research, and educational applications. The wealth of information aggregated with its quantification capabilities can augment or replace numerous textbook chapters. Five applications of the vascular model were described. The model is easily extendable in content, parcellation, and labeling, and the proposed approach is applicable for building a whole body vascular system.