MAGPI: A framework for maximum likelihood MR phase imaging using multiple receive coils.

Area of Science:

• Magnetic Resonance Imaging
• Medical Physics
• Image Processing

Background:

• Combining MR phase images from multiple coils is complex due to phase wrapping, noise, and coil phase offsets.
• Existing methods often require reference scans or suboptimal post-processing.

Purpose of the Study:

• To develop a rigorous, optimal framework for combining multi-coil MR phase data.
• To address challenges of phase wrapping, noise, and unknown phase offsets.

Main Methods:

• Formulated phase estimation using a maximum-likelihood (ML) approach.
• Developed a jointly optimized acquisition-processing chain: single multiecho gradient echo scan and voxel-per-voxel ML estimator.
• Introduced the Maximum AmbiGuity distance for Phase Imaging (MAGPI) framework.

Main Results:

• MAGPI achieves substantial improvements in phase estimation.
• Demonstrated phase signal-to-noise ratio (SNR) gains up to an order of magnitude compared to existing methods.

Conclusions:

• MAGPI provides ML-optimal combination of multi-coil phase data without reference scans.
• Enables voxel-per-voxel ML-optimal phase estimation, eliminating need for phase unwrapping or smoothing.
• Offers robust dynamic estimation of channel-dependent phase offsets.