Adaptive Respiratory Motion Correction Framework for Ungated Hepatic 4D Flow MRI.

Area of Science:

• Medical Imaging
• Cardiovascular Imaging
• Magnetic Resonance Imaging

Background:

• Respiratory motion significantly degrades the quality of ungated 4D flow MRI, particularly for hepatic applications.
• Artifacts impede accurate flow quantification and visualization of small vessels in the liver.
• Existing methods often require longer scan times or complex gating techniques.

Purpose of the Study:

• To develop and validate an adaptive respiratory motion correction framework (AMOCO) for ungated hepatic 4D flow MRI.
• To improve image quality, flow quantification accuracy, and small-vessel visualization.
• To maintain predictable and clinically acceptable scan times.

Main Methods:

• Developed an adaptive respiratory motion correction framework (AMOCO) incorporating optimized sampling, adaptive data selection, and motion correction.
• Evaluated AMOCO in healthy volunteers and a liver cirrhosis patient on 5T and 3T systems.
• Compared AMOCO-reconstructed ungated 4D flow MRI with 2D flow and navigator-gated 4D flow references.

Main Results:

• AMOCO achieved clinically acceptable scan times (5:30 min) with image quality comparable to navigator-gated acquisitions.
• Flow measurements using AMOCO showed insignificant net flow differences and improved consistency with 2D flow (ICC: 0.96).
• The method demonstrated feasibility across 5T and 3T scanning platforms.

Conclusions:

• AMOCO effectively suppresses motion artifacts in ungated hepatic 4D flow MRI.
• The framework enhances small-vessel visualization and improves the accuracy of flow measurements.
• AMOCO is a feasible technique for motion correction across different field strengths, maintaining clinical relevance.