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Graf, R.* ; Möller, H.J.* ; Starck, S.* ; Atad, M.* ; Braun, P.* ; Stelter, J.* ; Peters, A. ; Krist, L.* ; Willich, S.N.* ; Völzke, H.* ; Bülow, R.* ; Pischon, T.* ; Niendorf, T.* ; Paetzold, J.C.* ; Karampinos, D.C.* ; Rueckert, D.* ; Kirschke, J.*

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

In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 328-338 (Lect. Notes Comput. Sc. ; 15972 LNCS)
DOI
Volume Interpolated Breath-Hold Examination (VIBE) MRI generates images suitable for water and fat signal composition estimation. While the two-point VIBE provides rapid 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 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.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Mri ; Proton Density Fat Fraction ; Water-fat Mri ; Water-fat Swaps
Sprache englisch
Veröffentlichungsjahr 2026
HGF-Berichtsjahr 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Konferzenzdatum 23-27 September 2025
Konferenzort Daejeon
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15972 LNCS, Heft: , Seiten: 328-338 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-010
DOI 10.1007/978-3-032-05169-1_32
Scopus ID 105018065257
Erfassungsdatum 2025-10-23
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