Entropy-Based SVM Classifier for Automatic Detection of Motion Artifacts in Clinical MRI

Abstract

The extended acquisition time of Magnetic Resonance Imaging (MRI) makes it susceptible to image artifacts caused by subject motion. Artifact presence reduces diagnostic confidence and it could also necessitate a re-scan or an additional examination in extreme cases. Automatic artifact detection at the modality could improve the effciency, reliability and reproducibility of image quality verification. It could also prevent patient recall for additional examination due to unsatisfactory image quality. In this study we evaluate a machine learning method for the automatic detection of motion artifacts in order to instantly recognise problematic acquisitions before the patient has left the scanner. The paper proposes the use of local entropy estimation in the feature extraction stage of the chosen Support Vector Machine (SVM) classifier. Availability of sufficiently large training data set is one of the main constraints in training machine learning models. In order to enable training a model that could detect motion artifacts of varying severity, the paper also proposes a framework for generation of synthetic motion artifatcs in head MRI. On a per-slices basis, the implemented SVM classifier achieved an accuracy of 93.5% in the detection of motion artifacts in clinical MR images.