Purpose To investigate flip angle (FA)-dependent T1 bias in chemical shift-encoded fat-fraction (FF) and to evaluate a strategy for correcting this bias to achieve accurate MRI-based estimates of liver fat with optimized signal-to-noise ratio (SNR). Materials and Methods Thirty-three obese patients, 14 men/19 women, aged 57.3 ±13.9 years underwent 3 Tesla (T) liver MRI including MR-spectroscopy and four three-echo-complex chemical shift-encoded MRI sequences using different FAs (1°/3°/10°/20°). FF was estimated with R2* correction and multi-peak fat spectral modeling. The FF for each FA with and without T1 correction was compared with spectroscopy as a reference standard, using linear regression. Relative SNR of the magnitude data were assessed for each flip angle. Results The correlation between chemical shift-encoded MRI and spectroscopy was high (R2 ≍ 0.9). Without T1 correction, the agreement of both techniques showed no significant differences in slope (PFlipAngle1° = 0.385/PFlipAngle3° = 0.289) using low FA. High FA resulted in significant different slopes (PFlipAngle10°= 0.016/PFlipAngle20° = 0.014. T1 bias was successfully corrected using the T1 correction strategy (slope:PFlipAngle10° = 0.387/PFlipAngle20° = 0.440). Additionally, the use of high FA (near the Ernst angle) improved the SNR of the magnitude data (FA1 vs. FA3; respectively FA1 vs. FA10 P ≤ 0.001). Conclusion T1 bias is a strong confounder in the assessment of liver fat using chemical shift imaging with high FA. However, using a larger flip angle with T1 correction leads to higher SNR, and residual error after T1 correction is very small.
Purpose To investigate the feasibility of estimating the proton-density fat fraction (PDFF) using a 7.1 Tesla magnetic resonance imaging (MRI) system and to compare the accuracy of liver fat quantification using different fitting approaches. Materials and Methods Fourteen leptin-deficient ob/ob mice and eight intact controls were examined in a 7.1 Tesla animal scanner using a 3-dimensional six-echo chemical shift-encoded pulse sequence. Confounder-corrected PDFF was calculated using magnitude (magnitude data alone) and combined fitting (complex and magnitude data). Differences between fitting techniques were compared using Bland-Altman analysis. In addition, PDFFs derived with both reconstructions were correlated with histopathological fat content and triglyceride mass fraction using linear regression analysis. Results The PDFFs determined with use of both reconstructions correlated very strongly (r=0.91). However, small mean bias between reconstructions demonstrated divergent results (3.9%; CI 2.7%-5.1%). For both reconstructions, there was linear correlation with histopathology (combined fitting: r=0.61; magnitude fitting: r=0.64) and triglyceride content (combined fitting: r=0.79; magnitude fitting: r=0.70). Conclusion Liver fat quantification using the PDFF derived from MRI performed at 7.1 Tesla is feasible. PDFF has strong correlations with histopathologically determined fat and with triglyceride content. However, small differences between PDFF reconstruction techniques may impair the robustness and reliability of the biomarker at 7.1 Tesla.
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