Multisite, multivendor validation of the accuracy and reproducibility of proton‐density fat‐fraction quantification at 1.5T and 3T using a fat–water phantom

• Published in: Magnetic resonance in medicine Vol. 77; no. 4; pp. 1516 - 1524
• Main Authors: Hernando, Diego, Sharma, Samir D., Aliyari Ghasabeh, Mounes, Alvis, Bret D., Arora, Sandeep S., Hamilton, Gavin, Pan, Li, Shaffer, Jean M., Sofue, Keitaro, Szeverenyi, Nikolaus M., Welch, E. Brian, Yuan, Qing, Bashir, Mustafa R., Kamel, Ihab R., Rice, Mark J., Sirlin, Claude B., Yokoo, Takeshi, Reeder, Scott B.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01-04-2017
• Subjects: Adipose Tissue - diagnostic imaging; Body Water - diagnostic imaging; chemical shift‐encoded; Equipment Design; Equipment Failure Analysis; fat quantification; Magnetic Resonance Imaging - instrumentation; multicenter; nonalcoholic fatty liver disease; phantom; Phantoms, Imaging; Protons; proton‐density fat‐fraction (PDFF); quantitative imaging biomarker; Reproducibility of Results; Sensitivity and Specificity; chemical shift-encoded; phantom; nonalcoholic fatty liver disease; multicenter; proton-density fat-fraction (PDFF); quantitative imaging biomarker; fat quantification
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.26228

Purpose
To evaluate the accuracy and reproducibility of quantitative chemical shift‐encoded (CSE) MRI to quantify proton‐density fat‐fraction (PDFF) in a fat–water phantom across sites, vendors, field strengths, and protocols.
Methods
Six sites (Philips, Siemens, and GE Healthcare) participated in this study. A phantom containing multiple vials with various oil/water suspensions (PDFF:0%–100%) was built, shipped to each site, and scanned at 1.5T and 3T using two CSE protocols per field strength. Confounder‐corrected PDFF maps were reconstructed using a common algorithm. To assess accuracy, PDFF bias and linear regression with the known PDFF were calculated. To assess reproducibility, measurements were compared across sites, vendors, field strengths, and protocols using analysis of covariance (ANCOVA), Bland–Altman analysis, and the intraclass correlation coefficient (ICC).
Results
PDFF measurements revealed an overall absolute bias (across sites, field strengths, and protocols) of 0.22% (95% confidence interval, 0.07%–0.38%) and R2 > 0.995 relative to the known PDFF at each site, field strength, and protocol, with a slope between 0.96 and 1.02 and an intercept between −0.56% and 1.13%. ANCOVA did not reveal effects of field strength (P = 0.36) or protocol (P = 0.19). There was a significant effect of vendor (F = 25.13, P = 1.07 × 10−10) with a bias of −0.37% (Philips) and −1.22% (Siemens) relative to GE Healthcare. The overall ICC was 0.999.
Conclusion
CSE‐based fat quantification is accurate and reproducible across sites, vendors, field strengths, and protocols. Magn Reson Med 77:1516–1524, 2017. © 2016 International Society for Magnetic Resonance in Medicine