Automated Identification of Eye Motion in Raw MRI Data Using Machine Learning

Magnetic resonance imaging (MRI) is invaluable for the detailed visualization of soft tissues. However, its susceptibility to motion artifacts presents a challenge in ophthalmology due to the continuous eye movement. A recently developed technique effectively resolves eye motion in MRI, but it strongly relies on an eye tracker (ET), which, being a resource-intensive system, limits its broader adoption. The present work introduces a novel approach that, based on machine learning techniques, enables automated identification of eye motion in raw MRI data collected using a fast-sampling acquisition strategy. Such MRI data were acquired from nine healthy subjects while visual stimuli directed their gaze. A synchronized ET signal was also recorded to label the data. A broad spectrum of features representing the raw MRI acquisitions were extracted, and the classification models were built based on the most discriminative ones. Motion identification was primarily driven by phase-related features, while the models achieved accuracy and recall values exceeding 98%. This study represents an important first step towards obtaining a high-quality MRI of the eye without depending on supplementary hardware.