Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Magnetic Resonance Materials in Physics, Biology and Medicine

13-02-2024 | Magnetic Resonance Imaging | Research Article

Linearity and bias of proton density fat fraction across the full dynamic range of 0–100%: a multiplatform, multivendor phantom study using 1.5T and 3T MRI at two sites

Authors: Houchun H. Hu, Henry Szu-Meng Chen, Diego Hernando

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine

Login to get access

Abstract

Objective

Performance assessments of quantitative determinations of proton density fat fraction (PDFF) have largely focused on the range between 0 and 50%. We evaluate PDFF in a two-site phantom study across the full 0–100% PDFF range.

Materials and methods

We used commercially available 3D chemical-shift-encoded water-fat MRI sequences from three MRI system vendors at 1.5T and 3T and conducted the study across two sites. A spherical phantom housing 18 vials spanning the full 0–100% PDFF range was used. Data at each site were acquired using default parameters to determine same-day and different-day intra-scanner repeatability, and inter-system and inter-site reproducibility, in addition to linear regression between reference and measured PDFF values.

Results

Across all systems, results demonstrated strong linearity and minimal bias. For 1.5T systems, a pooled slope of 0.99 with a 95% confidence interval (CI) of 0.981–0.997 and a pooled intercept of 0.61% PDFF with a 95% CI of 0.17–1.04 were obtained. Results for pooled 3T data included a slope of 1.00 (95% CI 0.995–1.005) and an intercept of 0.69% PDFF (95% CI 0.39–0.97). Inter-site and inter-system reproducibility coefficients ranged from 2.9 to 6.2 (in units of PDFF), while intra-scanner same-day and different-day repeatability ranged from 0.6 to 7.8.

Discussion

PDFF across the 0–100% range can be reliably estimated using current commercial offerings at 1.5T and 3T.
Appendix
Available only for authorised users
Literature
1.
2.
Kupczyk PA, Kurt D, Endler C et al (2023) MRI proton density fat fraction for estimation of tumor grade in steatotic hepatocellular carcinoma. Eur Radiol 33(12):8974–8985PubMedPubMedCentralCrossRef
3.
Gu J, Liu S, Du S, Zhang Q, Xiao J, Dong Q, Xin Y (2019) Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. Eur Radiol 29(7):3564–3573PubMedCrossRef
4.
Qu Y, Li M, Hamilton G, Zhang YN, Song B (2019) Diagnostic accuracy of hepatic proton density fat fraction measured by magnetic resonance imaging for the evaluation of liver steatosis with histology as reference standard: a meta-analysis. Eur Radiol 29(10):5180–5189PubMedCrossRef
5.
Fowler KJ, Venkatesh SK, Obuchowski N et al (2023) Repeatability of MRI biomarkers in nonalcoholic fatty liver disease: the NIMBLE consortium. Radiology 309(1):e231092PubMedCrossRef
6.
Nedrud MA, Chaudhry M, Middleton MS et al (2023) MRI quantification of placebo effect in nonalcoholic steatohepatitis clinical trials. Radiology 306(3):e220743PubMedCrossRef
7.
Caussy C, Reeder SB, Sirlin CB, Loomba R (2018) Noninvasive, quantitative assessment of liver fat by MRI-PDFF as an endpoint in NASH trials. Hepatology 68(2):763–772PubMedCrossRef
8.
Hernando D, Sharma SD, Ghasabeh MA, et al (2017) Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom. Magn Reson Med. 77(4):1516–1524.
9.
Kim HJ, Cho HJ, Kim B, You MW, Lee JH, Huh J, Kim JK (2019) Accuracy and precision of proton density fat fraction measurement across field strengths and scan intervals: a phantom and human study. J Magn Reson Imaging 50(1):305–314PubMedCrossRef
10.
Bachtiar V, Kelly MD, Wilman HR et al (2019) Repeatability and reproducibility of multiparametric magnetic resonance imaging of the liver. PLoS ONE 14(4):e0214921PubMedPubMedCentralCrossRef
11.
Jang JK, Lee SS, Kim B et al (2019) Agreement and reproducibility of proton density fat fraction measurements using commercial MR sequences across different platforms: a multivendor, multi-institutional phantom experiment. Invest Radiol 54(8):517–523PubMedCrossRef
12.
Schneider E, Remer EM, Obuchowski NA, McKenzie CA, Ding X, Navaneethan SD (2021) Long-term inter-platform reproducibility, bias, and linearity of commercial PDFF MRI methods for fat quantification: a multi-center, multi-vendor phantom study. Eur Radiol 31(10):7566–7574PubMedCrossRef
13.
Zhao R, Hernando D, Harris DT et al (2021) Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom. Med Phys 48(8):4375–4386PubMedCrossRef
14.
Orcel T, Chau HT, Turlin B et al (2023) Evaluation of proton density fat fraction (PDFF) obtained from a vendor-neutral MRI sequence and MRQuantif software. Eur Radiol 33(12):8999–9009PubMedCrossRef
15.
Artz NS, Haufe WM, Hooker CA, et al (2015) Reproducibility of MR-based liver fat quantification across field strength: Same-day comparison between 1.5T and 3T in obese subjects. J Magn Reson Imaging 42(3):811–817PubMedPubMedCentralCrossRef
16.
Sofue K, Mileto A, Dale BM, Zhong X, Bashir MR (2015) Interexamination repeatability and spatial heterogeneity of liver iron and fat quantification using MRI-based multistep adaptive fitting algorithm. J Magn Reson Imaging 42(5):1281–1290PubMedCrossRef
17.
Johnson BL, Schroeder ME, Wolfson T et al (2014) Effect of flip angle on the accuracy and repeatability of hepatic proton density fat fraction estimation by complex data-based, T1-independent, T2*-corrected, spectrum-modeled MRI. J Magn Reson Imaging 39(2):440–447PubMedCrossRef
18.
Wang X, Hernando D, Reeder SB (2016) Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum. Magn Reson Med 75(2):845–851PubMedCrossRef
19.
Hong CW, Cui JY, Batakis D et al (2021) Repeatability and accuracy of various region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification. Abdom Radiol (NY) 46(7):3105–3116PubMedCrossRef
20.
21.
Yokoo T, Serai SD, Pirasteh A et al (2018) Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR Imaging: a meta-analysis. Radiology 286(2):486–498PubMedCrossRef
22.
Hu HH, Yokoo T, Bashir M et al (2021) Linearity and bias of proton density fat fraction as a quantitative imaging biomarker: A multicenter, multiplatform, multivendor phantom study. Radiology 298(3):640–651PubMedCrossRef
23.
Obuchowski NA, Bucker A, Kinahan P et al (2016) Statistical issues in testing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) profile claims. Acad Radiol 23(4):496–506PubMedPubMedCentralCrossRef
24.
Kessler LG, Barnhart HX, Bucker AJ et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24(1):9–26MathSciNetPubMedCrossRef
25.
Sullivan DC, Na O, Kessler LG et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277(3):813–825PubMedCrossRef
26.
Swauger S, Fashho K, Hornung LN, Elder DA et al (2023) Association of pancreatic fat on imaging with pediatric metabolic co-morbidities. Pediatr Radiol 53(10):2030–2039PubMedCrossRef
27.
Story JD, Ghahremani S, Kafali SG et al (2023) Using free-breathing MRI to quantify pancreatic fat and investigate spatial heterogeneity in children. J Magn Reson Imaging 57(2):508–518PubMedCrossRef
28.
Nardo L, Karampinos DC, Lansdown DA et al (2014) Quantitative assessment of fat infiltration in the rotator cuff muscles using water-fat MRI. J Magn Reson Imaging 39(5):1178–1185PubMedCrossRef
29.
Schlaffke L, Rehmann R, Rohm M et al (2019) Multi-center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles. NMR in Biomed 32(9):e4119CrossRef
30.
Wang TY, Nie P, Zhao X et al (2022) Proton density fat fraction measurements of rotator cuff muscles: accuracy, repeatability, and reproducibility across readers and scanners. Magn Reson Imaging 92:260–267PubMedCrossRef
31.
Baum T, Yap SP, Dieckmeyer M et al (2015) (2015) Assessment of whole spine vertebral bone marrow fat using chemical shift-encoding based water-fat MRI. J Magn Reson Imaging 42(4):1083–1023CrossRef
32.
Schmeel FC, Vomweg T, Traber F et al (2019) Proton density fat fraction MRI of vertebral bone marrow: accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms. J Magn Reson Imaging 50:1762–1772PubMedCrossRef
33.
Burakiewicz J, Sinclair CDJ, Fischer D, Walter GA, Kan HE, Hollingsworth KG (2017) Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy. J Neurol 264(10):2053–2067PubMedPubMedCentralCrossRef
34.
Grimm A, Meyer H, Nickel MD et al (2018) Repeatability of Dixon magnetic resonance imaging and magnetic resonance spectroscopy for quantitative muscle fat assessments in the thigh. J Cachexia Sarcopenia Muscle 9(6):1093–1100PubMedPubMedCentralCrossRef
35.
Schlaeger S, Sollmann N, Zoffl A, Becherucci EA et al (2021) 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. Diagnostics (Basel) 11(6):1056PubMedCrossRef
36.
De Wel B, Huysmans L, Depuydt CE et al (2023) Histopathological correlations and fat replacement imaging patterns in recessive limb-girdle muscular dystrophy type 12. J Cachexia Sarcopenia Muscle 14(3):1468–1481PubMedPubMedCentralCrossRef
37.
Henze Bancroft LC, Strigel RM, Macdonald EB, Longhurst C, Johnson J, Hernando D, Reeder SB (2022) Proton density water fraction as a reproducible MR-based measurement of breast density. Magn Reson Med 87(4):1742–1757PubMedCrossRef
38.
Borde T, Wu M, Ruschke S et al (2023) Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3T MRI. Eur Radiol 33(6):3810–3818PubMedCrossRef
39.
Bamberg F, Kauczor HU, Weckbach S et al (2015) Whole-body MR imaging in the German national cohort: rationale, design, and technical background. Radiology 277(1):206–220PubMedCrossRef
40.
Bray TJP, Chouhan MD, Punwani S, Bainbridge A, Hall-Craggs MA (2018) Fat fraction mapping using magnetic resonance imaging: insight into pathophysiology. Br J Radiol 91(1089):20170344PubMed
41.
Linge J, Borga M, West J et al (2018) Body composition profiling in the UK Biobank imaging study. Obesity (Silver Spring) 26(11):1785–1795PubMedCrossRef
42.
Frantz D, Weidlich D, Freitag F et al (2018) Association of proton density fat fraction in adipose tissue with imaging-based and anthropometric obesity markers in adults. Int J Obes (Lond) 42(2):175–182CrossRef
43.
Wu M, Junker D, Branca RT, Karampinos DC (2020) Magnetic resonance imaging techniques for brown adipose tissue detection. Front Endocrinol (Lausanne) 11:421PubMedCrossRef
44.
Drabsch T, Junker D, Bayer S et al (2022) Association between adipose tissue proton density fat fraction, resting metabolic rate, and FTO genotype in humans. Front Endocrinol 13:804874CrossRef
45.
Idilman IS, Yildiz AE, Karaosmanoglu AD, Ozmen MN, Akata D, Karcaaltincaba M (2022) Proton density fat fraction: magnetic resonance imaging applications beyond the liver. Diagn Interv Radiol 28(1):83–91PubMedCrossRef
46.
GnatiucFriedrichs L, Trichia E, Aguilar-Ramirez D, Preiss D (2023) Metabolic profiling of MRI-measured liver fat in the UK Biobank. Obesity (Silver Spring) 31(4):1121–1132CrossRef
47.
Tipirneni-Sajja A, Brasher S, Shrestha U, Johnson H, Morin C, Satapathy S (2023) Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms. MAGMA 36(4):529–551PubMedCrossRef
48.
Bush EC, Gifford A, Coolbaugh CL, Towse TF, Damon BM, Welch EB (2018) Fat-water phantoms for magnetic resonance imaging validation: a flexible and scalable protocol. J Vis Exp 7:(139):57704
49.
Hines CDG, Yu H, Shimakawa A, McKenzie CA, Brittain JH, Reeder SB (2009) T1 independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: validation in a Fat-Water-SPIO phantom. J Magn Reson Imaging 30(5):1215–1222PubMedPubMedCentralCrossRef
50.
Hernando D, Sharma SD, Kramer H, Reeder SB (2014) On the confounding effect of temperature on chemical shift-encoded fat quantification. Magn Reson Med 72(2):464–470PubMedCrossRef
51.
Navaratna R, Zhao R, Colgan TJ et al (2021) Temperature-corrected proton density fat fraction estimation using chemical shift-encoded MRI in phantoms. Magn Reson Med 86(1):68–81CrossRef
52.
Meloni A, Pistoia L, Restaino G et al (2022) Quantitative T2* MRI for bone marrow iron overload: normal reference values and assessment in thalassemia major patients. Radiol Med 127(11):1199–1208PubMedCrossRef
53.
Leonhardt Y, Gassert F, Feuerriegel, et al (2021) Vertebral bone marrow T2* mapping using chemical shift encoding-based water-fat separation in the quantitative analysis of lumbar osteoporosis and osteoporotic fractures. Quant Imaging Med Surg 11(8):3715–3725PubMedPubMedCentralCrossRef
54.
Schwenzer NF, Machann J, Haap MM et al (2008) T2* relaxometry in liver, pancreas, and spleen in a healthy cohort of one hundred twenty-nine subjects-correlation with age, gender, and serum ferritin. Invest Radiol 43(12):854–860PubMedCrossRef
55.
Tang MY, Chen TW, Huang XH et al (2016) Acute pancreatitis with gradient echo T2*-weighted magnetic resonance imaging. Quant Imaging Med Surg 6(2):157–167PubMedPubMedCentralCrossRef
56.
Cunha GM, Kolokythas O, Chen W et al (2023) Intra-examination agreement between multi-echo gradient echo and confounder-corrected chemical shift-encoded MR sequences for R2* estimation as a biomarker of liver iron content in patients with a wide range of T2*/R2* and proton density fat fraction values. Abdom Radiol (NY) 48(7):2302–2310CrossRef
57.
Metadata
Title
Linearity and bias of proton density fat fraction across the full dynamic range of 0–100%: a multiplatform, multivendor phantom study using 1.5T and 3T MRI at two sites
Authors
Houchun H. Hu
Henry Szu-Meng Chen
Diego Hernando
Publication date
13-02-2024
Publisher
Springer International Publishing
Published in
Magnetic Resonance Materials in Physics, Biology and Medicine
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI
https://doi.org/10.1007/s10334-024-01148-9