Effect of image standardization on FLAIR MRI for brain extraction. Issue 1 (December 2015) - Reiche, Brittany; Moody, Alan; Khademi, April
Abstract Fluid attenuation inversion recovery (FLAIR) magnetic resonance images (MRI) are being used by physicians to identify and analyze white matter lesions in the brain to determine whether patients are at risk of stroke. Pipelines used to analyze these images require a preprocessing step of brain extraction in order to be robust and to be applied to multicenter, large-scale studies. This paper proposes a novel brain extraction tool solely for the FLAIR modality, as well as a robust standardization pipeline that eliminates variability between datasets by reducing image noise, intensity inhomogeneity, patient movement, and the nonstandardness of tissue intensities, which are inherent in MRI. Feature extraction is performed on the standardized dataset, and a brain segmentation is produced by a random forest classifier. The effects of the standardization steps are evaluated using objective metrics, and the resultant segmentations produced by the unstandardized and standardized images are compared. By implementing a robust standardization pipeline, images acquired from different scanners at different centers can be processed automatically and accurately, allowing for the fast processing of large-scale, multicenter data.