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

2025-02-20

AI Summary

Water–fat signal swapping in non-enhanced VIBE MRI causes severe errors in proton density fat fraction (PDFF) quantification. Method: We propose an end-to-end water–fat swap detection and correction framework comprising three novel components: (1) a Perlin-noise-augmented U-Net for high-accuracy swap detection (<1% error rate); (2) a denoising diffusion probabilistic model (DDPM) to learn water–fat signal priors; and (3) physics-constrained optimization incorporating the R2*–PDFF biophysical model. Contribution/Results: We report, for the first time, a significantly elevated prevalence of water–fat swapping in individuals with extreme BMI. After correction, PDFF estimation achieves high accuracy and reliability, substantially improving the robustness and generalizability of six-point VIBE for large-scale clinical and population-based imaging studies.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Detect water-fat swaps in VIBE MRI
• Correct swaps using a three-step pipeline
• Ensure accurate PDFF estimation in MRI

Innovation

• Automated water-fat swap detection pipeline
• Denoising diffusion image-to-image network
• Physics-constrained model signal recovery