Prospective 3D Fat Navigator (FatNav) motion correction for 7T Terra MRI

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging
• Neuroimaging

Background:

• Ultra-high field (7T) MRI offers superior resolution and signal-to-noise ratio (SNR).
• Patient motion is a significant challenge in 7T MRI, limiting clinical utility.
• Prospective motion correction is essential for certain advanced MRI acquisitions.

Purpose of the Study:

• To implement and evaluate prospective 3D Fat Navigator (FatNav) motion correction on a Siemens 7T Terra MRI system.
• To assess the impact of FatNav on image quality and motion artifact reduction in brain imaging.
• To develop a modular approach for integrating motion correction into existing MRI sequences.

Main Methods:

• Developed a modular Sequence Building Block for FatNav and integrated it into a gradient-recalled echo (GRE) sequence.
• Modified the reconstruction pipeline for online FatNav image reconstruction and motion update delivery.
• Tested multiple registration algorithms and implemented the most effective ones for online correction of T1 and T2* weighted brain images.

Main Results:

• The implemented FatNav system provides motion updates in under 3 seconds, suitable for real-time correction.
• Prospective FatNav significantly reduced motion artifacts, resulting in crisper images.
• Quantitative analysis showed a 1.59x improvement in image sharpness (variance of Laplacian) and 33% steeper profiles across the cerebral falx for corrected images.

Conclusions:

• Prospective 3D FatNav motion correction effectively enhances GRE image quality in the brain at 7T MRI.
• The modular Sequence Building Block facilitates straightforward integration of motion correction into 7T MRI pulse sequences.
• This advancement is crucial for realizing the full clinical potential of 7T MRI by overcoming motion-related limitations.