Prospective motion correction for 3D GRASE pCASL with volumetric navigators

Area of Science:

• Medical Imaging
• Neuroimaging
• Magnetic Resonance Imaging

Background:

• Motion artifacts are a significant challenge in background suppressed segmented 3D GRASE pCASL imaging.
• Existing motion correction methods can introduce delays or alter image contrast.
• Accurate perfusion imaging is crucial for diagnosing and monitoring various neurological conditions.

Purpose of the Study:

• To develop and validate a prospective motion correction approach for background suppressed segmented 3D GRASE pCASL.
• To assess the effectiveness of volumetric EPI-based navigators (vNavs) in reducing motion artifacts.
• To evaluate the impact of motion correction on temporal signal-to-noise ratio (t-SNR) and image quality.

Main Methods:

• Implementation of prospective motion correction using volumetric EPI-based navigators (vNavs).
• Acquisition of background suppressed segmented 3D GRASE pCASL data with and without vNavs.
• Application of principle component analysis (PCA) to further refine motion correction.
• Quantitative and qualitative assessment of motion artifacts, t-SNR, and gyral structure restoration.

Main Results:

• vNavs effectively reduced motion artifacts in background suppressed segmented 3D GRASE pCASL.
• The proposed method demonstrated minimal contrast change and no additional scan time.
• vNavs significantly increased the temporal signal-to-noise ratio (t-SNR).
• Principle component analysis further reduced residual motion and improved the visualization of gyral details.

Conclusions:

• Prospective motion correction using vNavs is a feasible and effective strategy for background suppressed segmented 3D GRASE pCASL.
• This approach enhances image quality and reliability without compromising scan efficiency or contrast.
• The combination of vNavs and PCA offers a robust solution for motion-related artifacts in perfusion imaging.