Bloody noise: The impact of blood-flow artifacts on registration.

Abstract

Blood-flow artifacts present a serious challenge for most, if not all, volumetric analytical approaches. We utilize T1-weighted data with prominent blood-flow artifacts from the Autism Brain Imaging Data Exchange (ABIDE) multisite agglomerative dataset to assess the impact that such blood-flow artifacts have on registration of T1-weighted data to a template. We use a heuristic approach to identify the blood-flow artifacts in these data; we use the resulting blood masks to turn the underlying voxels to the intensity of the cerebro-spinal fluid, thus mimicking the effect of blood suppression. We then register both the original data and the deblooded data to a common T1-weighted template, and compare the quality of those registrations to the template in terms of similarity to the template. The registrations to the template based on the deblooded data yield significantly higher similarity values compared with those based on the original data. Additionally, we measure the nonlinear deformations needed to transform the data from the position achieved by registering the original data to the template to the position achieved by registering the deblooded data to the template. The results indicate that blood-flow artifacts may seriously impact data processing that depends on registration to a template, that is, most all data processing.