MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI

TL;DR

This paper presents an automated pipeline for detecting and correcting water-fat swaps in VIBE MRI images, improving the accuracy of PDFF estimation crucial for clinical and population studies.

Contribution

The study introduces a novel three-step method combining segmentation, diffusion-based correction, and physics modeling to address water-fat swaps in VIBE MRI.

Findings

Achieves less than 1% error rate in swap detection.

Swaps mainly affect underweight and obese individuals.

Enables reliable PDFF estimation for large-scale analysis.

Abstract

Volume Interpolated Breath-Hold Examination (VIBE) MRI generates images suitable for water and fat signal composition estimation. While the two-point VIBE provides water-fat-separated images, the six-point VIBE allows estimation of the effective transversal relaxation rate R2* and the proton density fat fraction (PDFF), which are imaging markers for health and disease. Ambiguity during signal reconstruction can lead to water-fat swaps. This shortcoming challenges the application of VIBE-MRI for automated PDFF analyses of large-scale clinical data and of population studies. This study develops an automated pipeline to detect and correct water-fat swaps in non-contrast-enhanced VIBE images. Our three-step pipeline begins with training a segmentation network to classify volumes as "fat-like" or "water-like," using synthetic water-fat swaps generated by merging fat and water volumes with Perlin noise. Next, a denoising diffusion image-to-image network predicts water volumes as signal priors for correction. Finally, we integrate this prior into a physics-constrained model to recover accurate water and fat signals. Our approach achieves a < 1% error rate in water-fat swap detection for a 6-point VIBE. Notably, swaps disproportionately affect individuals in the Underweight and Class 3 Obesity BMI categories. Our correction algorithm ensures accurate solution selection in chemical phase MRIs, enabling reliable PDFF estimation. This forms a solid technical foundation for automated large-scale population imaging analysis.