MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI
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2025
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| author | Graf, Robert Möller, Hendrik Starck, Sophie Atad, Matan Braun, Philipp Stelter, Jonathan Peters, Annette Krist, Lilian Willich, Stefan N. Völzke, Henry Bülow, Robin Berger, Klaus Pischon, Tobias Niendorf, Thoralf Paetzold, Johannes Karampinos, Dimitrios Rueckert, Daniel Kirschke, Jan |
| author_facet | Graf, Robert Möller, Hendrik Starck, Sophie Atad, Matan Braun, Philipp Stelter, Jonathan Peters, Annette Krist, Lilian Willich, Stefan N. Völzke, Henry Bülow, Robin Berger, Klaus Pischon, Tobias Niendorf, Thoralf Paetzold, Johannes Karampinos, Dimitrios Rueckert, Daniel Kirschke, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI Graf, Robert Möller, Hendrik Starck, Sophie Atad, Matan Braun, Philipp Stelter, Jonathan Peters, Annette Krist, Lilian Willich, Stefan N. Völzke, Henry Bülow, Robin Berger, Klaus Pischon, Tobias Niendorf, Thoralf Paetzold, Johannes Karampinos, Dimitrios Rueckert, Daniel Kirschke, Jan Computer Vision and Pattern Recognition 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. |
| title | MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.14659 |