Automated eye blink detection and correction method for clinical MR eye imaging

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Biomedical Engineering
• Ophthalmology

Background:

• Ocular MRI is susceptible to artifacts caused by involuntary movements like eye blinks.
• Motion artifacts degrade image quality, hindering accurate diagnosis and research.
• Existing methods for motion correction are often post-processing based and less effective for real-time issues.

Purpose of the Study:

• To develop and validate an online monitoring system for detecting eye blinks during ocular MRI.
• To implement an automatic feedback system for real-time reacquisition of corrupted k-space data caused by blinks.
• To improve the quality of ocular MRI by mitigating blink-induced artifacts.

Main Methods:

• Utilized fluorine-based field probes positioned near the eye to detect magnetic field shifts caused by blinks.
• Integrated a real-time feedback system with the 7 Tesla MRI scanner.
• Subjects were instructed to blink during scanning, and corrupted k-space lines were automatically reacquired.

Main Results:

• The implemented system successfully detected eye blinks via field probes.
• Uncorrected MRI images showed severe artifacts due to blinks.
• Corrected images, using reacquired data, achieved image quality comparable to blink-free scans.

Conclusions:

• Field probes are effective for real-time eye blink detection during MRI.
• Automatic reacquisition of blink-corrupted data significantly enhances ocular MRI quality.
• This system enables high-quality MR imaging of the eye, crucial for clinical and research applications.