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

Abstract

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.

Keywords: chemical shift-encoded; fat quantification; multicenter; nonalcoholic fatty liver disease; phantom; proton-density fat-fraction (PDFF); quantitative imaging biomarker.

© 2016 International Society for Magnetic Resonance in Medicine.

Figures

Figure 1

Workflow of the data processing algorithm used for PDFF mapping.

Figure 2

Workflow of the phase correction algorithm used in this work for bipolar acquisitions. This algorithm performs phase correction along the readout direction, seeking the linear phase correction ϕ(x)= ϕ0+xϕ1 (applied to the even echoes) that results in the best match between fat-water separated images obtained from complex- and magnitude-fitting, respectively. The algorithm is initialized by sampling a discrete grid on the space of ϕ0 and ϕ1. Starting from the optimum point within the initial grid, the method then applies a descent algorithm to find a locally optimum solution where complex-fitting and magnitude-fitting provide the most similar fat-water separations.

Figure 3

Phantom PDFF mapping demonstrates accurate fat quantification at all sites, vendors, field strengths and protocols. A) Representative PDFF map. B) Linear regression analysis showing high correlation, slope close to 1 and intercept close to 0 for all acquisitions.

Figure 4

Bland-Altman analysis comparing PDFF measurements across protocols and across field strengths demonstrate reproducible fat quantification, for all sites and vendors in this study.

Figure 5

Bland-Altman analysis between PDFF measurements from sites 2–6 and those from site 1 (measured at the beginning of the project) demonstrates reproducible fat quantification with low bias across sites. Bland-Altman analysis between PDFF measurements from site 1 at the beginning and end of the project demonstrates integrity of the phantom and lack of drift in PDFF values.