Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images.

ABSTRACT:

Background

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction.

Methodology/principal findings

Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebra

SUBMITTER: Seghier ML 

PROVIDER: S-EPMC3063172 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature