Proton Density Fat Fraction Measurements at 1.5- and 3-T Hepatic MR Imaging: Same-Day Agreement among Readers and across Two Imager Manufacturers - Serai, Suraj D.; Dillman, Jonathan R.; Trout, Andrew T. (Radiology, 2017)
Purpose To determine the agreement of proton density fat fraction (PDFF) measurements obtained with hepatic magnetic resonance (MR) imaging among readers, imager manufacturers, and field strengths. Materials and Methods This HIPAA-compliant study was approved by the institutional review board. After providing informed consent, 24 adult volunteers underwent imaging with one 1.5-T MR unit (Ingenia; Philips Healthcare, Best, the Netherlands) and two different 3.0-T units (750 W [GE Healthcare, Waukesha, Wis] and Ingenia) on the same day to estimate hepatic PDFF. A single-breath-hold multipoint Dixon-based acquisition was performed with commercially available pulse sequences provided by the MR imager manufacturers (mDIXON Quant [Philips Healthcare], IDEAL IQ [GE Healthcare]). Five readers placed one large region of interest, inclusive of as much liver parenchyma as possible in the right lobe while avoiding large vessels, on imager-generated parametric maps to measure hepatic PDFF. Two-way single-measure intraclass correlation coefficients (ICCs) were used to assess interreader agreement and agreement across the three imaging platforms. Results Excellent interreader agreement for hepatic PDFF measurements was obtained with mDIXON Quant and the Philips 1.5-T unit (ICC, 0.995; 95% confidence interval [CI]: 0.991, 0.998), mDIXON Quant and the Philips 3.0-T unit (ICC, 0.992; 95% CI: 0.986, 0.996), and IDEAL IQ and the GE 3.0-T unit (ICC, 0.966; 95% CI: 0.939, 0.984). Individual reader ICCs for hepatic PDFF measurements across all three imager manufacturer-field strength combinations also showed excellent interimager agreement, ranging from 0.914 to 0.954. Conclusion Estimation of PDFF with hepatic MR imaging by using multipoint Dixon techniques is highly reproducible across readers, field strengths, and imaging platforms. RSNA, 2017.