Abstract: Abnormalities of blood vessels associated with brains of individuals can be detected by analyzing images that include the brains of the individuals. Blood vessels included in the images can be identified and densities of blood vessels across hemispheres can be determined. Differences between densities of blood vessels in different hemispheres of the brains of the individuals can be used to determine a probability of an abnormality with respect to one or more blood vessels associated with the brains of the individuals. User interfaces can be generated indicating possible abnormalities associated with the brains of the individuals.

Abstract: Automated AIF/VOF identification is useful in connection with providing rapid and automatic quantitative imaging results. Examples include CBF (cerebral blood flow) and CBV (cerebral blood volume) perfusion maps. In the present work, AIF/VOF regions of images are identified by making use of a predefined anatomic vasculature template to provide numerical weighting values for voxels. A score is computed for each voxel that includes normalized signal fit parameters and the numerical weighting. AIF/VOF voxels are identified as N voxels having the highest score, where N can depend on the image resolution.