{ "id": "epj5bxgdpz", "slug": "water-fat-separation-mri", "title": "Water–fat separation (MRI)", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health" }, { "id": "life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical" }, { "id": "life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging" }, { "id": "life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques" } ], "geo": [ { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Non-geographic · 1946 to 2000: Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life" }, { "id": "geo.nongeo.t1946.life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health" }, { "id": "geo.nongeo.t1946.life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical" }, { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging" }, { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques" } ] } ], "excerpt": "Water–fat separation is an MRI post-processing method, based on W T Dixon's 1984 technique, that separates water and fat signals to quantify tissue fat, especially in the liver.", "snippet": "Water–fat separation is an MRI post-processing method, based on W T Dixon's 1984 technique, that separates water and fat signals to quantify tissue fat, especially in the liver.", "node": "life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "markdown": "# Water–fat separation (MRI)\n\nWater–fat separation is an MRI post-processing method that decomposes each voxel's signal into separate water and fat maps to quantify tissue fat and generate fat-suppressed or fat-only images. In its quantitative form it also produces a proton density fat fraction (PDFF) map, most prominently in the liver.\n\n| Key fact | Value |\n|---|---|\n| Physical basis | Water protons precess ~3.5 ppm faster than fat; ~210 Hz at 1.5 T, ~420 Hz at 3 T[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf) |\n| Outputs | Water-only, fat-only, in-phase, and opposed-phase images, plus PDFF and \\( R_{2}^{*} \\) maps[2](https://www.ajronline.org/doi/10.2214/AJR.07.3182) |\n| Standard quantitative acquisition | 4–6 equally spaced gradient echoes, ΔTE below half the water–fat phase cycle (~2.3 ms at 1.5 T, ~1.15 ms at 3 T)[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf) |\n| Agreement with MR spectroscopy | \\( R^{2} = 0.96 \\); regression slope 0.99 at both 1.5 T and 3 T[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/) • [5](https://cds.ismrm.org/protected/10MProceedings/PDFfiles/4630_7163.PDF) |\n| Reproducibility | Coefficient of reproducibility 4.1%, repeatability 3.0%[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/) |\n| Iron limit | PDFF unreliable when liver \\( R_{2}^{*} \\) exceeds ~300 s⁻¹ at 1.5 T or ~450 s⁻¹ at 3 T[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf) |\n| Origin | Two-point method published by W T Dixon, Radiology, 1984[6](https://doi.org/10.1148/radiology.153.1.6089263) |\n\n## How it works\n\nThe method exploits chemical shift: the main fat spectral peak, dominated by methylene and terminal methyl protons, resonates about 3.5 ppm below water.[7](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492) At 1.5 T this is roughly 210 Hz, so fat and water magnetizations cycle between in-phase and opposed-phase alignment approximately every 2.2 ms.[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf) • [8](https://ajronline.org/doi/10.2214/AJR.13.10606)\n\nEach echo's phase therefore encodes the water-to-fat ratio. The general chemical-shift-encoded model is\n\n\\[ s(\\mathbf{r}; TE_{n}) = \\left[ s_{W}(\\mathbf{r}) + s_{F}(\\mathbf{r})\\, e^{i 2\\pi f_{cs} TE_{n}} \\right] e^{i 2\\pi \\psi(\\mathbf{r}) TE_{n}} \\]\n\nwith the known water–fat frequency difference \\( f_{cs} \\) (the −3.5 ppm shift converted to Hz at the field strength, so that \\( f_{cs} TE_{n} \\) is dimensionless) and three unknowns per voxel: water signal, fat signal, and the \\( B_{0} \\) field map \\( \\psi \\), the off-resonance frequency in Hz.[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf) In the original two-point form the in-phase image is \\( s_{0} = s_{W} + s_{F} \\), so water and fat images follow by summation and subtraction.[7](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492) Quantitative pipelines add \\( T_{2}^{*} \\) decay and a multi-peak fat spectrum to the model, and report PDFF \\( = \\rho_{F}/(\\rho_{W} + \\rho_{F}) \\); appropriately designed acquisition and correction steps reduce, but do not inherently eliminate, \\( T_{1} \\), flip-angle, and noise bias.[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf)\n\n## How it is done\n\nThe operator selects a multi-echo gradient-echo (or, for qualitative imaging, fast spin echo or SSFP) sequence and the reconstruction pipeline then runs these steps[2](https://www.ajronline.org/doi/10.2214/AJR.07.3182):\n\n1. Acquire two or more echoes at echo times chosen so water and fat are in and out of phase (at 1.5 T, in-phase TEs 0, 4.6, 9.2 ms; opposed 2.3, 6.9, 11.5 ms).[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf)\n2. Estimate the \\( B_{0} \\) field map by phase unwrapping (two- and three-point Dixon) or by iterative least-squares fitting over candidate field values (IDEAL).[7](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492)\n3. Solve the least-squares water/fat problem per voxel, with a graph cut algorithm to enforce spatially smooth field maps and prevent swaps.[9](https://doi.org/10.1002/mrm.22177)\n4. For quantitative PDFF, acquire 4–6 echoes with low flip angles (~10° 2D, 3°–5° 3D) and correct \\( T_{1} \\) bias, \\( T_{2}^{*} \\) decay, multi-peak fat spectrum, noise, and inter-echo phase errors; maps are output as [DICOM](https://www.edgepedia.org/dicom) scaled 0–100%.[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf)\n\nA whole-liver PDFF map reconstructs in about 15–20 s within one breath hold.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/)\n\n## Origin\n\nDixon's 1984 paper, \"Simple proton spectroscopic imaging,\" modified a spin echo sequence to acquire an in-phase and an opposed-phase image at 0.35 T, where the shift is about 50 Hz, producing water, fat, opposed, and conventional images.[6](https://doi.org/10.1148/radiology.153.1.6089263) Because two points cannot separate field inhomogeneity from composition, Yeung and Kormos corrected the field in situ in 1986 without extra imaging time[10](https://doi.org/10.1148/radiology.159.3.3704157), and Borrello and colleagues added regional phase correction in 1987.[11](https://doi.org/10.1148/radiology.164.2.3602397) Glover and Schneider's 1991 three-point technique computed a field-inhomogeneity image alongside true water and fat images[12](https://doi.org/10.1002/mrm.1910180211), and Glover's companion multipoint paper generalized the technique to additional phase encodings.[13](https://doi.org/10.1002/jmri.1880010504) Coombs, Szumowski, and Coshow revisited the two-point method with phase unwrapping in 1997[14](https://doi.org/10.1002/mrm.1910380606), and Ma's 2004 dual-echo breath-hold technique with robust region-growing phase correction brought two-point Dixon into abdominal practice.[15](https://doi.org/10.1002/mrm.20146)\n\nThe modern iterative family grew from Reeder and colleagues' 2003 multicoil iterative least-squares separation[16](https://doi.org/10.1002/mrm.10675) into IDEAL, reported by Scott B. Reeder and colleagues in 2005.[17](https://doi.org/10.1002/mrm.20624) Pineda, Reeder, Wen, and Pelc's 2005 Cramér–Rao analysis showed the asymmetric echo angles that maximize noise performance.[18](https://doi.org/10.1002/mrm.20623) Yu and colleagues added simultaneous \\( T_{2}^{*} \\) estimation in 2007[19](https://doi.org/10.1002/jmri.21090) and multi-peak fat spectrum modeling with simultaneous \\( R_{2}^{*} \\) estimation in 2008[20](https://doi.org/10.1002/mrm.21737); Hernando, Kellman, Haldar, and Liang introduced the graph cut formulation for large field inhomogeneities in 2009.[9](https://doi.org/10.1002/mrm.22177)\n\n## Variants\n\n**Two-point Dixon** acquires only in-phase and opposed-phase images, saving one third of the time relative to three-point, but its effective number of signal averages is 2 and it is vulnerable to fat–water swaps.[7](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492) • [8](https://ajronline.org/doi/10.2214/AJR.13.10606) **Three-point Dixon** adds a field map (effective NSA 2.67; SNR efficiency 95% of a 3-NEX acquisition) and tolerates off-resonance from susceptibility, demagnetization, and shim error.[12](https://doi.org/10.1002/mrm.1910180211) Sampling at (0, 2π/3, 4π/3) raises effective NSA to 3.[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf) **IDEAL** uses the asymmetric angles (−π/6+πk, π/2+πk, 7π/6+πk), giving noise performance independent of the water/fat ratio, and works with essentially any pulse sequence.[17](https://doi.org/10.1002/mrm.20624) • [2](https://www.ajronline.org/doi/10.2214/AJR.07.3182) Related developments include Xiang's partially-opposed-phase asymmetric two-point method (2006)[21](https://doi.org/10.1002/mrm.20984), Hardy, Hinks, and Tkach's three-point fast spin echo implementation (1995)[22](https://doi.org/10.1002/jmri.1880050213), hierarchical multiresolution IDEAL[23](https://doi.org/10.1002/mrm.24441), and compressed-sensing acceleration with parallel imaging.[24](https://doi.org/10.1002/mrm.24270) MDWF-Net, a multi-task U-Net with self-attention reported by Juan Pablo Meneses and colleagues in 2023, estimated liver PDFF from 3 echoes with slope 0.94 and \\( R^{2} \\) 0.97 against the graph cut reference, cutting nominal scan time from 120 to 54 s.[25](https://doi.org/10.1007/s00330-023-09576-2)\n\n## Applications\n\nClinical uses include hepatic fat quantification and characterization of fat-containing liver lesions, detection of intracytoplasmic lipid in adrenal adenomas (the basis of a specific MRI diagnosis of adenoma), and uniform fat suppression across the head and neck, breast, heart, abdomen, pelvis, and extremities; the technique is insensitive to \\( B_{1} \\) inhomogeneity.[2](https://www.ajronline.org/doi/10.2214/AJR.07.3182) Because the fit yields fat-corrected \\( R_{2}^{*} \\) (\\( R_{2}^{*} = 1/T_{2}^{*} \\)), a single acquisition quantifies coexisting liver fat and iron in hemochromatosis and [hemosiderosis](https://www.edgepedia.org/hemosiderosis).[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/) A 2024 multivendor phantom study (18 vials, nominal PDFF 0–100%) showed accurate PDFF across the full range with commercial LiverLab and mDIXON Quant sequences at 1.5 T and 3 T without parameter modification, with repeatability ~3% and inter-scanner, inter-site, and inter-vendor reproducibility averaging 4%.[26](https://pmc.ncbi.nlm.nih.gov/articles/PMC11428149/)\n\n## Limitations and alternatives\n\n**Fat–water swaps** arise because with only two points there is ambiguity about whether water or fat dominates a voxel; phase wrapping begins when field inhomogeneity exceeds about 1.75 ppm, half the water–fat shift.[8](https://ajronline.org/doi/10.2214/AJR.13.10606) • [7](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492) In three-point Dixon the field-map estimate wraps within [−π, π], and \\( T_{2}/T_{2}^{*} \\) decay at longer echoes compounds the problem.[1](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf) Simple in/opposed-phase reading is ambiguous above 50% fat: a 30% fat fraction causes the same signal dropout as 70%.[2](https://www.ajronline.org/doi/10.2214/AJR.07.3182) Magnitude-based reconstruction is robust to phase errors but limited to PDFF 0–50%; complex-based reconstruction covers 0–100% with higher SNR but is more sensitive to phase errors and swaps.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/) Other confounders are \\( T_{1} \\) bias, eddy currents, noise bias, concomitant gradients, temperature, and fat's multi-peak spectrum[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/); relaxation effects in gradient-echo fat quantification were analyzed by Bydder and colleagues in 2008.[27](https://doi.org/10.1016/j.mri.2007.08.012) Iron overload shortens \\( T_{2}^{*} \\), in extreme cases to 2–3 ms, and PDFF is unreliable when liver \\( R_{2}^{*} \\) exceeds ~300 s⁻¹ at 1.5 T or ~450 s⁻¹ at 3 T.[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf) Echo count matters: two-echo in/opposed-phase methods without \\( R_{2}^{*} \\) and multi-peak correction show a regression slope of 0.85 against MRS-reference PDFF, while six-echo acquisition with both corrections reaches 0.98.[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf) The QIBA profile claims a 95% CI within ±8% absolute of true PDFF cross-sectionally, and that a measured change of ±5% or more indicates true change with >95% confidence[26](https://pmc.ncbi.nlm.nih.gov/articles/PMC11428149/); the QIBA PDFF Profile standardizes hepatic PDFF as a quantitative biomarker at both field strengths.[3](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf) Compared with single-voxel MRS, CSE MRI covers the whole organ in one acquisition but fits a model to overlapping spectral peaks; in a published meta-analysis, ultrasound and CT show lower specificity ranges for steatosis diagnosis (70–85% and 88–95%) than confounder-corrected MRS (92–96%).[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/)\n\n## References\n\n1. [Fat-Water MRI lecture slides (UCLA, 2025)](https://labs.dgsom.ucla.edu/file/656931/m229_2025_fatwater_zhong_guestlecture_05272025.pdf)\n2. [Body MRI Using IDEAL (AJR)](https://www.ajronline.org/doi/10.2214/AJR.07.3182)\n3. [QIBA Proton Density Fat Fraction (PDFF) Profile](https://qibawiki.rsna.org/images/5/55/QIBA_PDFF_Profile_Stage2_FINAL.pdf)\n4. [Quantification of Liver Fat Content with CT and MRI: State of the Art](https://pmc.ncbi.nlm.nih.gov/articles/PMC8574059/)\n5. [Reproducibility of MRI-Determined PDFF across MR Scanner Platforms and Field Strength (ISMRM 2010)](https://cds.ismrm.org/protected/10MProceedings/PDFfiles/4630_7163.PDF)\n6. [W T Dixon (1984). Simple proton spectroscopic imaging.. Radiology.](https://doi.org/10.1148/radiology.153.1.6089263)\n7. [Dixon techniques for water and fat imaging (Ma, J Magn Reson Imaging 2008)](https://onlinelibrary.wiley.com/doi/10.1002/jmri.21492)\n8. [Fat-water separation techniques (AJR)](https://ajronline.org/doi/10.2214/AJR.13.10606)\n9. [Diego Hernando and colleagues (2009). Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.22177)\n10. [H N Yeung, D W Kormos (1986). Separation of true fat and water images by correcting magnetic field inhomogeneity in situ.. Radiology.](https://doi.org/10.1148/radiology.159.3.3704157)\n11. [J A Borrello and colleagues (1987). Chemical shift-based true water and fat images: regional phase correction of modified spin-echo MR images.. Radiology.](https://doi.org/10.1148/radiology.164.2.3602397)\n12. [G. H. Glover, E. Schneider (1991). Three‐point dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.1910180211)\n13. [Gary H. Glover (1991). Multipoint dixon technique for water and fat proton and susceptibility imaging. Journal of Magnetic Resonance Imaging.](https://doi.org/10.1002/jmri.1880010504)\n14. [Bernard D. Coombs, Jerzy Szumowski, William Coshow (1997). Two‐point Dixon technique for water‐fat signal decomposition with B0 inhomogeneity correction. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.1910380606)\n15. [Jingfei Ma (2004). Breath‐hold water and fat imaging using a dual‐echo two‐point dixon technique with an efficient and robust phase‐correction algorithm. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.20146)\n16. [Scott B. Reeder and colleagues (2003). Multicoil Dixon chemical species separation with an iterative least‐squares estimation method. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.10675)\n17. [Scott B. Reeder and colleagues (2005). Iterative decomposition of water and fat with echo asymmetry and least‐squares estimation (IDEAL): Application with fast spin‐echo imaging. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.20624)\n18. [Angel R. Pineda and colleagues (2005). Cramér–Rao bounds for three‐point decomposition of water and fat. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.20623)\n19. [Huanzhou Yu and colleagues (2007). Multiecho reconstruction for simultaneous water‐fat decomposition and T2* estimation. Journal of Magnetic Resonance Imaging.](https://doi.org/10.1002/jmri.21090)\n20. [Huanzhou Yu and colleagues (2008). Multiecho water‐fat separation and simultaneous R estimation with multifrequency fat spectrum modeling. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.21737)\n21. [Qing‐San Xiang (2006). Two‐point water‐fat imaging with partially‐opposed‐phase (POP) acquisition: An asymmetric Dixon method. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.20984)\n22. [Peter A. Hardy, R. Scott Hinks, Jean A. Tkach (1995). Separation of fat and water in fast spin‐echo MR imaging with the three‐point dixon technique. Journal of Magnetic Resonance Imaging.](https://doi.org/10.1002/jmri.1880050213)\n23. [Jeffrey Tsao, Yun Jiang (2012). Hierarchical IDEAL: Fast, robust, and multiresolution separation of multiple chemical species from multiple echo times. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.24441)\n24. [Samir D. Sharma, Houchun H. Hu, Krishna S. Nayak (2012). Chemical shift encoded water–fat separation using parallel imaging and compressed sensing. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.24270)\n25. [Juan Pablo Meneses and colleagues (2023). Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes. European Radiology.](https://doi.org/10.1007/s00330-023-09576-2)\n26. [Linearity and Bias of PDFF Across the Full Dynamic Range of 0–100%: Multiplatform, Multivendor Phantom Study (2024)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11428149/)\n27. [Mark Bydder and colleagues (2008). Relaxation effects in the quantification of fat using gradient echo imaging. Magnetic Resonance Imaging.](https://doi.org/10.1016/j.mri.2007.08.012)\n\n---\n*Topic: Encyclopedia › Life and health › Human health and medicine › Clinical assessment and procedures › Medical imaging and radiography › Magnetic resonance imaging techniques*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/water-fat-separation-mri", "markdown_url": "https://www.edgepedia.org/water-fat-separation-mri.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Water–fat separation (MRI)\", Edgepedia (EdgeChat), https://www.edgepedia.org/water-fat-separation-mri. Edgepedia Community License 1.0.", "credit_md": "\"[Water–fat separation (MRI)](https://www.edgepedia.org/water-fat-separation-mri)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/water-fat-separation-mri](https://www.edgepedia.org/water-fat-separation-mri). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Water–fat separation (MRI)\", Edgepedia (EdgeChat), https://www.edgepedia.org/water-fat-separation-mri. Edgepedia Community License 1.0.", "speakable": "Water–fat separation is an MRI post-processing method, based on W T Dixon's 1984 technique, that separates water and fat signals to quantify tissue fat, especially in the liver." }