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Research progress of magnetic resonance proton density fat fraction in extra-hepatic fat quantification
KUANG Lanqiong  CHENG Yu  ZHU Yong  YANG Lixia 

Cite this article as: KUANG L Q, CHENG Y, ZHU Y, et al. Research progress of magnetic resonance proton density fat fraction in extra-hepatic fat quantification[J]. Chin J Magn Reson Imaging, 2026, 17(9): 199-206. DOI:10.12015/issn.1674-8034.2026.09.026.


[Abstract] Magnetic resonance proton density fat fraction (MR-PDFF) is a non-invasive, accurate and reproducible fat quantification technique. It has been widely applied in the research of hepatic fat metabolism and related diseases. However, fat metabolic disorders and ectopic fat deposition are not confined to the liver, they also involve multiple extra-hepatic tissues, including the pancreas, kidneys, vertebral bone marrow, paraspinal muscles and skeletal muscles, and are closely associated with the onset and progression of various metabolic, digestive, osteoarticular diseases and malignant tumors. This paper briefly describes the basic principles of MR-PDFF technology and systematically elaborates its application value and research progress in fat quantification of extra-hepatic tissues such as the pancreas, kidneys, vertebral bone marrow and muscles, so as to provide references for early screening, mechanistic research and precise diagnosis and treatment of extra-hepatic fat-related diseases.
[Keywords] magnetic resonance imaging;fat fraction;proton density;pancreas;kidney;vertebral bone marrow;muscle

KUANG Lanqiong1   CHENG Yu2   ZHU Yong1   YANG Lixia1*  

1 Department of Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai 200237, China

2 Department of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China

Corresponding author: YANG L X, E-mail: cocoding@sina.com

Conflicts of interest   None.

Received  2026-01-09
Accepted  2026-08-23
DOI: 10.12015/issn.1674-8034.2026.09.026
Cite this article as: KUANG L Q, CHENG Y, ZHU Y, et al. Research progress of magnetic resonance proton density fat fraction in extra-hepatic fat quantification[J]. Chin J Magn Reson Imaging, 2026, 17(9): 199-206. DOI:10.12015/issn.1674-8034.2026.09.026.

[1]
YAN S Y, YANG Y W, JIANG X Y, et al. Fat quantification: Imaging methods and clinical applications in cancer[J/OL]. Eur J Radiol, 2023, 164: 110851 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/37148843/. DOI: 10.1016/j.ejrad.2023.110851.
[2]
MIRANDA J, WAKATE TERUYA A KEY, LEÃO FILHO H, et al. Diffuse and focal liver fat: advanced imaging techniques and diagnostic insights[J]. Abdom Radiol (NY), 2024, 49(12): 4437-4462. DOI: 10.1007/s00261-024-04407-4.
[3]
XIA T Y, DU M L, LI H Q, et al. Association between liver MRI proton density fat fraction and liver disease risk[J/OL]. Radiology, 2023, 309: e231007 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/37874242/. DOI: 10.1148/radiol.231007.
[4]
YANG C, YU S H, XU F P, et al. Application of magnetic resonance imaging-proton density fat fraction in liver fat quantification[J]. J Clin Hepatol, 2024, 40(3): 600-605. DOI: 10.12449/JCH240327.
[5]
HIGASHI M, TANABE M, TANABE K, et al. Multiparametric magnetic resonance imaging findings of the pancreas: A comparison in patients with type 1 and 2 diabetes[J/OL]. Tomography, 2025, 11(2): 16 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/39997999/. DOI: 10.3390/tomography11020016.
[6]
PANC K, GUNDOGDU H, SEKMEN S, et al. Liver and pancreatic fat fractions as predictors of disease severity in acute pancreatitis: an MRI IDEAL-IQ study[J]. Abdom Radiol, 2025, 50(8): 3734-3743. DOI: 10.1007/s00261-025-04809-y.
[7]
YAMAZAKI H, STREICHER S A, WU L, et al. Evidence for a causal link between intra-pancreatic fat deposition and pancreatic cancer: A prospective cohort and Mendelian randomization study[J/OL]. Cell Rep Med, 2024, 5(2): 101391 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/38280379/. DOI: 10.1016/j.xcrm.2023.101391.
[8]
TANG H L, XIE L H, LIU L, et al. Renal fat deposition measured on Dixon-based MRI is significantly associated with early kidney damage in obesity[J]. Abdom Radiol (NY), 2024, 49(10): 3476-3484. DOI: 10.1007/s00261-024-04391-9.
[9]
FRIEDLI I, BAID-AGRAWAL S, UNWIN R, et al. Magnetic resonance imaging in clinical trials of diabetic kidney disease[J/OL]. J Clin Med, 2023, 12(14): 4625 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/37510740/. DOI: 10.3390/jcm12144625.
[10]
GASSERT F T, KUFNER A, GASSERT F G, et al. MR-based proton density fat fraction (PDFF) of the vertebral bone marrow differentiates between patients with and without osteoporotic vertebral fractures[J]. Osteoporos Int, 2022, 33(2): 487-496. DOI: 10.1007/s00198-021-06147-3.
[11]
MOU Z L, YI W F, LUO M B, et al. Association of lumbar disc herniation and paraspinal muscles changes in patients with chronic low back pain[J]. J Back Musculoskelet Rehabil, 2025, 38(3): 567-575. DOI: 10.1177/10538127241305888.
[12]
LIU D, LIN C R, LIU B D, et al. Quantification of fat Metaplasia in the sacroiliac joints of patients with axial spondyloarthritis by chemical shift-encoded MRI: A diagnostic trial[J/OL]. Front Immunol, 2021, 12: 811672 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/35116037/. DOI: 10.3389/fimmu.2021.811672.
[13]
GARCIA-DIEZ A I, PORTA-VILARO M, ISERN-KEBSCHULL J, et al. Myosteatosis: diagnostic significance and assessment by imaging approaches[J]. Quant Imaging Med Surg, 2024, 14(11): 7937-7957. DOI: 10.21037/qims-24-365.
[14]
Kuru Ö D, Ellik Z, Gürsoy Ç A, et al. Assessing hepatic steatosis by magnetic resonance in potential living liver donors[J]. Diagn Interv Radiol, 2024, 30(6): 351-356. DOI: 10.4274/dir.2024.242697.
[15]
KIM J W, LEE C H, YANG Z P, et al. The spectrum of magnetic resonance imaging proton density fat fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and two different histopathologic methods (artificial intelligence vs. pathologist) in quantifying hepatic steatosis[J]. Quant Imaging Med Surg, 2022, 12(11): 5251-5262. DOI: 10.21037/qims-22-393.
[16]
DYKE J P. Quantitative MRI proton density fat fraction: A coming of age[J]. Radiology, 2021, 298(3): 652-653. DOI: 10.1148/radiol.2020204356.
[17]
YOSHIZAWA E, YAMADA A. MRI-derived proton density fat fraction[J]. J Med Ultrasonics, 2021, 48(4): 497-506. DOI: 10.1007/s10396-021-01135-w.
[18]
BRZESKA B, SABISZ A, KOZAK O, et al. Comparison of MR spectroscopy, 2-point Dixon, and multi-echo T2* sequences in assessing hepatic fat fraction across a diverse range of body mass index (BMI) and waist circumference ratio (WCR) values[J]. Diabetes Metab Syndr Obes, 2025, 18: 601-614. DOI: 10.2147/DMSO.S481062.
[19]
MA M Y, WANG J Y, LI X B, et al. Research progress and applications of fat quantification with magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2023, 14(7): 197-202. DOI: 10.12015/issn.1674-8034.2023.07.036.
[20]
WU S Z, CHEN F. Clinical application and research progress in MRI-based quantification of hepatic steatosis[J]. J China Clin Med Imaging, 2025, 36(8): 596-599. DOI: 10.12117/jccmi.2025.08.015.
[21]
REEDER S B, PINEDA A R, WEN Z F, et al. Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): Application with fast spin-echo imaging[J]. Magn Reson Med, 2005, 54(3): 636-644. DOI: 10.1002/mrm.20624.
[22]
SUN K F, TANG Y X, ZHANG W S, et al. A comparative analysis between mDixon quant and FACT techniques in fat quantification of vertebrae and paravertebral muscle[J]. Chin Comput Med Imaging, 2024, 30(5): 571-575. DOI: 10.3969/j.issn.1006-5741.2024.05.008.
[23]
RUGIVARODOM M, GEERATRAGOOL T, PAUSAWASDI N, et al. Fatty pancreas: linking pancreas pathophysiology to nonalcoholic fatty liver disease[J]. J Clin Transl Hepatol, 2022, 10(6): 1229-1239. DOI: 10.14218/JCTH.2022.00085.
[24]
LIU Y, GAO L, WU M F, et al. Effect of adipose tissue deposition on insulin resistance in middle-aged and elderly women: Based on QCT and MRI mDIXON-Quant[J]. J Diabetes Investig, 2025, 16(2): 292-297. DOI: 10.1111/jdi.14352.
[25]
QI X, ZHANG F, YAO W J, et al. MRI measurements of pancreatic fat content in the general population and their relationship with age, gender, and body mass index[J/OL]. Medicine, 2026, 105(13): e48133 [2026-02-12]. https://pubmed.ncbi.nlm.nih.gov/41894307/. DOI: 10.1097/MD.0000000000048133.
[26]
YANG J Z, ZHAO J C, NEMATI R, et al. An adapted deep convolutional neural network for automatic measurement of pancreatic fat and pancreatic volume in clinical multi-protocol magnetic resonance images: A retrospective study with multi-ethnic external validation[J/OL]. Biomedicines, 2022, 10(11): 2991 [2026-02-12]. https://pubmed.ncbi.nlm.nih.gov/36428558/. DOI: 10.3390/biomedicines10112991.
[27]
HEBER S D, HETTERICH H, LORBEER R, et al. Pancreatic fat content by magnetic resonance imaging in subjects with prediabetes, diabetes, and controls from a general population without cardiovascular disease[J/OL]. PLoS One, 2017, 12(5): e0177154 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/28520813/. DOI: 10.1371/journal.pone.0177154.
[28]
YASOKAWA K, KANKI A, NAKAMURA H, et al. Changes in pancreatic exocrine function, fat and fibrosis in diabetes mellitus: analysis using MR imaging[J/OL]. Br J Radiol, 2023, 96(1145): 20210515 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/36961451/. DOI: 10.1259/bjr.20210515.
[29]
GJELA M, ASKELAND A, MELLERGAARD M, et al. Intra-pancreatic fat deposition and its relation to obesity: a magnetic resonance imaging study[J]. Scand J Gastroenterol, 2024, 59(6): 742-748. DOI: 10.1080/00365521.2024.2333365.
[30]
YU X, HUANG Y H, FENG Y Z, et al. Well-controlled versus poorly controlled diabetes in patients with obesity: differences in MRI-evaluated pancreatic fat content[J]. Quant Imaging Med Surg, 2023, 13(6): 3496-3507. DOI: 10.21037/qims-22-1083.
[31]
SOTOZONO H, KANKI A, YASOKAWA K, et al. Value of 3-T MR imaging in intraductal papillary mucinous neoplasm with a concomitant invasive carcinoma[J]. Eur Radiol, 2022, 32(12): 8276-8284. DOI: 10.1007/s00330-022-08881-6.
[32]
YANG S, YAO W J, ZHENG S S. The effect of region of interest size in quantitative evaluation of pancreatic fat using IDEAL-IQ[J]. J Pract Radiol, 2021, 37(7): 1183-1187, 1191. DOI: 10.3969/j.issn.1002-1671.2021.07.032.
[33]
KATO S, IWASAKI A, KURITA Y, et al. Three-dimensional analysis of pancreatic fat by fat-water magnetic resonance imaging provides detailed characterization of pancreatic steatosis with improved reproducibility[J/OL]. PLoS One, 2019, 14(12): e0224921 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/31790429/. DOI: 10.1371/journal.pone.0224921.
[34]
STORY J D, GHAHREMANI S, KAFALI S G, et al. UsingFree-breathing MRIto quantify pancreatic fat and investigate spatial heterogeneity in children[J]. Magnetic Resonance Imaging, 2023, 57(2): 508-518. DOI: 10.1002/jmri.28337.
[35]
SONODA Y, FUJISAWA S, KUROKAWA M, et al. Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images[J]. Jpn J Radiol, 2025, 43(10): 1663-1669. DOI: 10.1007/s11604-025-01814-5.
[36]
HU H T, LIANG W, ZHANG Z W, et al. The utility of perirenal fat in determining the risk of onset and progression of diabetic kidney disease[J/OL]. Int J Endocrinol, 2022, 2022(1): 2550744 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/36507087/. DOI: 10.1155/2022/2550744.
[37]
LIU J, CHEN H Z, TIAN C, et al. Renal ectopic fat deposition and hemodynamics in type 2 diabetes mellitus assessment with magnetic resonance imaging[J/OL]. Insights Imaging, 2025, 16(1): 93 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/40287889/. DOI: 10.1186/s13244-025-01971-1.
[38]
WANG Y, JU Y, AN Q, et al. mDIXON-Quant for differentiation of renal damage degree in patients with chronic kidney disease[J/OL]. Front Endocrinol (Lausanne), 2023, 14: 1187042 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/37547308/. DOI: 10.3389/fendo.2023.1267914.
[39]
LIU J, WU Y, TIAN C, et al. Quantitative assessment of renal steatosis in patients with type 2 diabetes mellitus using the iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence imaging: repeatability and clinical implications[J]. Quant Imaging Med Surg, 2024, 14(10): 7341-7352. DOI: 10.21037/qims-24-330.
[40]
CHENG J M, LUO W X, PAN J, et al. Renal ectopic lipid deposition in rats with early-stage diabetic nephropathy evaluated by the MR mDixon-Quant technique: association with the expression of SREBP-1 and PPARα in renal tissue[J]. Quant Imaging Med Surg, 2023, 13(7): 4504-4513. DOI: 10.21037/qims-22-1167.
[41]
ALTAY C, BAŞARA AKıN I, ÖZGÜL H A, et al. Is fat quantification based on proton density fat fraction useful for differentiating renal tumor types [J]. Abdom Radiol, 2025, 50(3): 1254-1265. DOI: 10.1007/s00261-024-04596-y.
[42]
GJELA M, ASKELAND A, FRØKJÆR J B, et al. MRI-based quantification of renal fat in obese individuals using different image analysis approaches[J]. Abdom Radiol (NY), 2022, 47(10): 3546-3553. DOI: 10.1007/s00261-022-03603-4.
[43]
BORELLI C, VERGARA D, GUGLIELMI R, et al. Assessment of bone marrow fat by 3-Tesla magnetic resonance spectroscopy in patients with chronic kidney disease[J]. Quant Imaging Med Surg, 2023, 13(11): 7432-7443. DOI: 10.21037/qims-23-530.
[44]
CHENG X G, LI K, ZHANG Y, et al. The accurate relationship between spine bone density and bone marrow in humans[J/OL]. Bone, 2020, 134: 115312 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/32145459/. DOI: 10.1016/j.bone.2020.115312.
[45]
ZHANG W, LIAO R P, YE H Y, et al. Correlation study of age, gender and lumbar vertebral marrow fat in adults based on MRI iterative decomposition of water and fat with echo asymmetry and least-squares estimation image quantitation imaging technique[J]. Chin J Spine Spinal Cord, 2024, 34(2): 121-127. DOI: 10.3969/j.issn.1004-406X.2024.02.02.
[46]
PACCOU J, BADR S, LOMBARDO D, et al. Bone marrow adiposity and fragility fractures in postmenopausal women: The ADIMOS case-control study[J]. J Clin Endocrinol Metab, 2023, 108(10): 2526-2536. DOI: 10.1210/clinem/dgad195.
[47]
KWACK K S, LEE H D, JEON S W, et al. Comparison of proton density fat fraction, simultaneous R2*, and apparent diffusion coefficient for assessment of focal vertebral bone marrow lesions[J]. Clin Radiol, 2020, 75(2): 123-130. DOI: 10.1016/j.crad.2019.09.141.
[48]
SCHMEEL F C, ENKIRCH S J, LUETKENS J A, et al. Diagnostic accuracy of quantitative imaging biomarkers in the differentiation of benign and malignant vertebral lesions: Combination of diffusion-weighted and proton density fat fraction spine MRI[J]. Clin Neuroradiol, 2021, 31(4): 1059-1070. DOI: 10.1007/s00062-021-01009-1.
[49]
LI S J, WANG B, LIANG W W, et al. Associations between vertebral marrow proton density fat fraction and risk of prostate cancer[J/OL]. Front Endocrinol, 2022, 13: 874904 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/35498437/. DOI: 10.3389/fendo.2022.874904.
[50]
GASSERT F G, KRANZ J, GASSERT F T, et al. Longitudinal MR-based proton-density fat fraction (PDFF) and T2* for the assessment of associations between bone marrow changes and myelotoxic chemotherapy[J]. Eur Radiol, 2024, 34(4): 2437-2444. DOI: 10.1007/s00330-023-10189-y.
[51]
WANG J P, ZHAO Z H, LIANG D, et al. Paraspinal muscles and gluteus medius fat infiltration are both associated with lumbar disc herniation[J/OL]. Insights Imaging, 2025, 16(1): 176 [2026-01-20]. https://pubmed.ncbi.nlm.nih.gov/35498437/. DOI: 10.1186/s13244-025-02064-9.
[52]
HUANG Y L, WANG L, ZENG X M, et al. Association of paraspinal muscle CSA and PDFF measurements with lumbar intervertebral disk degeneration in patients with chronic low back pain[J/OL]. Front Endocrinol, 2022, 13: 792819 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/35721738/. DOI: 10.3389/fendo.2022.792819.
[53]
HESSE N, STOHLDREIER Y, SCHLAEGER S, et al. Association of breastfeeding duration with longitudinal changes in vertebral bone marrow, paraspinal muscle composition, and metabolic parameters in premenopausal women over five years[J/OL]. Eur J Radiol, 2026, 195: 112514 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/41330311/. DOI: 10.1016/j.ejrad.2025.112514.
[54]
HARADA S, GERSING A S, STOHLDREIER Y, et al. Associations of gestational diabetes and proton density fat fraction of vertebral bone marrow and paraspinal musculature in premenopausal women[J/OL]. Front Endocrinol, 2023, 14: 1303126 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/32145459/. DOI: 10.3389/fendo.2023.1303126.
[55]
PAOLETTI M, MONFORTE M, BARZAGHI L, et al. Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study[J/OL]. J Neurol, 2025, 272(4): 306 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/40172709/. DOI: 10.1007/s00415-025-13062-8.
[56]
DE WEL B, HUYSMANS L, DEPUYDT C E, et al. Histopathological correlations and fat replacement imaging patterns in recessive limb-girdle muscular dystrophy type 12[J]. J Cachexia Sarcopenia Muscle, 2023, 14(3): 1468-1481. DOI: 10.1002/jcsm.13234.
[57]
DE WEL B, HUYSMANS L, PEETERS R, et al. Prospective natural history study in 24 adult patients with LGMDR12 over 2 years of follow-up: Quantitative MRI and clinical outcome measures[J/OL]. Neurology, 2022, 99(6): e638-e649 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/35577579/. DOI: 10.1212/WNL.0000000000200708.
[58]
DE WEL B, ITERBEKE L, HUYSMANS L, et al. Lessons for future clinical trials in adults with Becker muscular dystrophy: Disease progression detected by muscle magnetic resonance imaging, clinical and patient-reported outcome measures[J/OL]. Eur J Neurol, 2024, 31(7): e16282 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/38504654/. DOI: 10.1111/ene.16282.
[59]
SCHLAEGER S, SOLLMANN N, ZOFFL A, et al. Quantitative muscle MRI in patients with neuromuscular diseases-association of muscle proton density fat fraction with semi-quantitative grading of fatty infiltration and muscle strength at the thigh region[J/OL]. Diagnostics (Basel), 2021, 11(6): 1056 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/34201303/. DOI: 10.3390/diagnostics11061056.
[60]
GREVE T, BURIAN E, ZOFFL A, et al. Regional variation of thigh muscle fat infiltration in patients with neuromuscular diseases compared to healthy controls[J]. Quant Imaging Med Surg, 2021, 11(6): 2610-2621. DOI: 10.21037/qims-20-1098.
[61]
WU L F, LIN L, CHEN Y, et al. Application of the MR proton density fat fraction technology in the quantitative evaluation of fat infiltration in the rotator cuff muscle group after supraspinatus tendon injury[J]. J Pract Radiol, 2025, 41(11): 1852-1856. DOI: 10.3969/j.issn.1002-1671.2025.11.020.
[62]
XIE K P, HUANG Y L, CHEN J X, et al. Quantitative MRI study of calf muscle area and fat content in patients with chronic ankle instability[J]. Chin J Magn Reson Imaging, 2024, 15(10): 129-135. DOI: 10.12015/issn.1674-8034.2024.10.022.
[63]
HOSTIN M A, OGIER A C, MICHEL C P, et al. The impact of fatty infiltration on MRI segmentation of lower limb muscles in neuromuscular diseases: a comparative study of deep learning approaches[J]. J Magn Reson Imaging, 2023, 58(6): 1826-1835. DOI: 10.1002/jmri.28708.
[64]
ARINGHIERI G, ASTREA G, MARFISI D, et al. Convolutional neural network-based automated segmentation of skeletal muscle and subcutaneous adipose tissue on thigh MRI in muscular dystrophy patients[J/OL]. J Funct Morphol Kinesiol, 2024, 9(3): 123 [2026-01-06]. https://pubmed.ncbi.nlm.nih.gov/39051284/. DOI: 10.3390/jfmk9030123.

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