Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

ABSTRACT: Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion-prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two-dimensional transverse T2-weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistic

SUBMITTER: Ding Y 

PROVIDER: S-EPMC6588009 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature