MAGO-SP: detection and correction of water-fat swaps in magnitude-only VIBE MRI

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Item Type:	Preprint
Title:	MAGO-SP: detection and correction of water-fat swaps in magnitude-only VIBE MRI
Creators Name:	Graf, R., Möller, H., Starck, S., Atad, M., Braun, P., Stelter, J., Peters, A., Krist, L., Willich, S.N., Völzke, H., Bülow, R., Berger, K., Pischon, T., Niendorf, T., Paetzold, J., Karampinos, D., Rueckert, D. and Kirschke, J.
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.
Keywords:	MRI, Water-Fat MRI, Water-Fat Swaps, Proton Density Fat Fraction
Source:	arXiv
Publisher:	Cornell University
Article Number:	2502.14659
Date:	20 February 2025
Official Publication:	https://doi.org/10.48550/arXiv.2502.14659
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