Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology and Hepatology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Ophthalmology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
ASH 2024 Annual Meeting 2024 ESMO Congress 2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Women’s Health Knowledge Hub Advances in Alzheimer’s

Navigating neuroimaging in Alzheimer disease care

PET imaging is playing an increasingly critical role in managing AD, but how can you effectively integrate it into clinical practice? Our expert-led program will empower you with practical strategies and real-world case studies.

This content is intended for healthcare professionals outside of the UK.
ADVANCED SEARCH
Log in
Top
European Radiology

Open Access 11-04-2025 | Prostate Cancer | Oncology

Influence of imaging method on fat fraction estimation for assessing bone marrow in metastatic prostate cancer

Authors: Yassine N. Azma, Nada Boci, Katarzyna Abramowicz, Luca Russo, Matthew R. Orton, Nina Tunariu, Dow-Mu Koh, Geoffrey Charles-Edwards, David J. Collins, Jessica M. Winfield

Published in: European Radiology

Login to get access

Abstract

Objective

This study aimed to assess the accuracy of fat fraction estimation with clinically available Dixon sequences in normal-appearing marrow and bone metastases in the pelvis of metastatic prostate cancer patients.

Methods

A prospective single-centre study was conducted with metastatic prostate cancer patients and healthy volunteers. Linearity and bias of fat fraction estimates from clinically available Dixon sequences were assessed against a 6-point PDw gradient echo (q-Dixon) sequence measuring the reference standard proton density fat fraction. Lesion fat fraction estimates were cross-compared using the Friedman test. Repeatability in volunteers was evaluated with Bland-Altman plots. Sensitivity of fat fraction estimates using TSE-Dixon sequences to specific absorption rate (SAR) related modifications were evaluated with correlation plots.

Results

Thirty-three patients were recruited for this study. Significant (p < 0.05) absolute bias (12.4%) was demonstrated in the T1-weighted (T1w) Dixon measurements against the q-Dixon. Significant differences (p < 0.05) between fat fraction estimates provided by the T1w Dixon and PDw Dixon sequences were observed in 13 active and 6 treated lesions. Repeatability coefficients for fat fraction estimates ranged from 5.9 to 9.0% in the pelvic tissues of healthy volunteers. Reduction of slice number with repetition time for SAR had the greatest effect, reaching a maximum difference in fat fraction of 14.7% from the q-Dixon for the T2w-TSE Dixon in bone marrow.

Conclusions

T1w Dixon methods can detect post-treatment changes but remain confounded by relaxation time biases. While all Dixon methods showed good repeatability, careful choice of SAR-related modifications is critical to maintaining accuracy for PD- and T2-weighted TSE sequences.

Key Points

Question The clinical validity of signal-weighted fat fraction estimates versus proton density fat fraction for characterising metastatic bone lesions has not been fully assessed.
Findings T1-weighted Dixon sequences in line with whole-body MRI international guidelines demonstrate significant fat fraction bias, particularly in lesions and muscle.
Clinical relevance Fat fraction estimation using T1-weighted Dixon sequences recommended in international guidelines are highly sensitive to relaxation time biases, making underlying physiological changes potentially ambiguous.

Graphical Abstract

Appendix
Available only for authorised users
Literature
1.
Padhani AR, Lecouvet FE, Tunariu N et al (2017) METastasis Reporting and Data System for Prostate Cancer: practical guidelines for acquisition, interpretation, and reporting of whole-body magnetic resonance imaging-based evaluations of multiorgan involvement in advanced prostate cancer. Eur Urol 71:81–92CrossRefPubMedPubMedCentral
2.
Messiou C, Hillengass J, Delorme S et al (2019) Guidelines for acquisition, interpretation, and reporting of whole-body MRI in myeloma: myeloma response assessment and diagnosis system (MY-RADS). Radiology 291:5–13CrossRefPubMed
3.
Perez-Lopez R, Nava Rodrigues D, Figueiredo I et al (2018) Multiparametric magnetic resonance imaging of prostate cancer bone disease. Invest Radiol 53:96–102CrossRefPubMedPubMedCentral
4.
Latifoltojar A, Hall-Craggs M, Rabin N et al (2017) Whole body magnetic resonance imaging in newly diagnosed multiple myeloma: early changes in lesional signal fat fraction predict disease response. Br J Haematol 176:222–233CrossRefPubMed
5.
Donners R, Figueiredo I, Tunariu N et al (2022) Multiparametric bone MRI can improve CT-guided bone biopsy target selection in cancer patients and increase diagnostic yield and feasibility of next-generation tumour sequencing. Eur Radiol 32:4647–4656CrossRefPubMedPubMedCentral
6.
7.
Kühn J-P, Jahn C, Hernando D et al (2014) T1 bias in chemical shift-encoded liver fat-fraction: role of the flip angle. J Magn Reson Imaging 40:875–883CrossRefPubMed
8.
Liu C-Y, McKenzie CA, Yu H, Brittain JH, Reeder SB (2007) Fat quantification with IDEAL gradient echo imaging: correction of bias from T1 and noise. Magn Reson Med 58:354–364CrossRefPubMed
9.
Horng DE, Hernando D, Hines CDG, Reeder SB (2013) Comparison of R2* correction methods for accurate fat quantification in fatty liver. J Magn Reson Imaging 37:414–422CrossRefPubMed
10.
Reeder SB, Pineda AR, Wen Z et al (2005) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 54:636–644CrossRefPubMed
11.
Roberts NT, Hernando D, Holmes JH, Wiens CN, Reeder SB (2018) Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI. Magn Reson Med 80:685–695CrossRefPubMedPubMedCentral
12.
13.
Reeder SB, Robson PM, Yu H et al (2009) Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 29:1332–1339CrossRefPubMedPubMedCentral
14.
Peterson P, Månsson S (2014) Fat quantification using multiecho sequences with bipolar gradients: investigation of accuracy and noise performance. Magn Reson Med 71:219–229CrossRefPubMed
15.
Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 34:729–749CrossRefPubMedPubMedCentral
16.
Bainbridge A, Bray TJP, Sengupta R, Hall-Craggs MA (2020) Practical approaches to bone marrow fat fraction quantification across magnetic resonance imaging platforms. J Magn Reson Imaging 52:298–306CrossRefPubMed
17.
Reeder SB, Hu HH, Sirlin CB (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36:1011–1014CrossRefPubMedPubMedCentral
18.
Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Kiefer B, Bashir MR (2014) Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 72:1353–1365CrossRefPubMed
19.
Chiabai O, Van Nieuwenhove S, Vekemans M-C et al (2022) Whole-body MRI in oncology: can a single anatomic T2 Dixon sequence replace the combination of T1 and STIR sequences to detect skeletal metastasis and myeloma? Eur Radiol 33:244–257CrossRefPubMedPubMedCentral
20.
Maeder Y, Dunet V, Richard R, Becce F, Omoumi P (2018) Bone marrow metastases: T2-weighted Dixon spin-echo fat images can replace T1-weighted spin-echo images. Radiology 286:948–959CrossRefPubMed
21.
Danner A, Brumpt E, Alilet M, Tio G, Omoumi P, Aubry S (2019) Improved contrast for myeloma focal lesions with T2-weighted Dixon images compared to T1-weighted images. Diagn Interv Imaging 100:513–519CrossRefPubMed
22.
Deoni SCL, Peters TM, Rutt BK (2005) High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 53:237–241CrossRefPubMed
23.
Blackledge MD, Collins DJ, Koh DM, Leach MO (2016) Rapid development of image analysis research tools: bridging the gap between researcher and clinician with pyOsiriX. Comput Biol Med 69:203–212CrossRefPubMedPubMedCentral
24.
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310CrossRefPubMed
25.
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 286:486–498CrossRefPubMed
26.
Gee CS, Nguyen JTK, Marquez CJ et al (2015) Validation of bone marrow fat quantification in the presence of trabecular bone using MRI. J Magn Reson Imaging 42:539–544CrossRefPubMed
27.
Reeder SB, McKenzie CA, Pineda AR et al (2007) Water–fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 25:644–652CrossRefPubMed
28.
29.
30.
de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC (2004) MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 230:652–659CrossRefPubMed
31.
Weigel M, Hennig J (2006) Contrast behavior and relaxation effects of conventional and hyperecho-turbo spin echo sequences at 1.5 and 3 T. Magn Reson Med 55:826–835CrossRefPubMed
32.
Naarding KJ, Reyngoudt H, Van Zwet EW et al (2020) MRI vastus lateralis fat fraction predicts loss of ambulation in Duchenne muscular dystrophy. Neurology 94:e1386–e1394CrossRefPubMedPubMedCentral
33.
Kwack KS, Lee HD, Jeon SW, Lee HY, Park S (2020) Comparison of proton density fat fraction, simultaneous R2*, and apparent diffusion coefficient for assessment of focal vertebral bone marrow lesions. Clin Radiol 75:123–130CrossRefPubMed
34.
Schmeel FC, Luetkens JA, Wagenhäuser PJ et al (2018) Proton density fat fraction (PDFF) MRI for differentiation of benign and malignant vertebral lesions. Eur Radiol 28:2397–2405CrossRefPubMed
35.
Onu M, Savu M, Lungu-Solomonescu C, Harabagiu I, Pop T (2001) Early MR changes in vertebral bone marrow for patients following radiotherapy. Eur Radiol 11:1463–1469CrossRefPubMed
36.
Choi MH, Lee SW, Kim HG et al (2021) 3D MR fingerprinting (MRF) for simultaneous T1 and T2 quantification of the bone metastasis: initial validation in prostate cancer patients. Eur J Radiol 144:109990CrossRefPubMed
37.
Schick F, Forster J, Einsele H, Weiss B, Lutz O, Claussen CD (1995) Magnetization transfer in hemopoietic bone marrow examined by localized proton spectroscopy. Magn Reson Med 34:792–802CrossRefPubMed
38.
Schabel MC, Morrell GR (2009) Uncertainty in T1 mapping using the variable flip angle method with two flip angles. Phys Med Biol 54:1–8CrossRef
39.
40.
Wang X, Hernando D, Reeder SB (2016) Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum. Magn Reson Med 75:845–851CrossRefPubMed
41.
Nikiforaki K, Manikis GC, Boursianis T, Marias K, Karantanas A, Maris TG (2017) The impact of spin coupling signal loss on fat content characterization in multi-echo acquisitions with different echo spacing. Magn Reson Imaging 38:6–12CrossRefPubMed
Metadata
Title
Influence of imaging method on fat fraction estimation for assessing bone marrow in metastatic prostate cancer
Authors
Yassine N. Azma
Nada Boci
Katarzyna Abramowicz
Luca Russo
Matthew R. Orton
Nina Tunariu
Dow-Mu Koh
Geoffrey Charles-Edwards
David J. Collins
Jessica M. Winfield
Publication date
11-04-2025
Publisher
Springer Berlin Heidelberg
Published in
European Radiology
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-025-11564-7