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Erschienen in:
08.07.2019 | Hepatobiliary
Detection of hepatic steatosis and iron content at 3 Tesla: comparison of two-point Dixon, quantitative multi-echo Dixon, and MR spectroscopy
Erschienen in: Abdominal Radiology | Ausgabe 9/2019
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Purpose
To compare qualitative results obtained from computer-aided dual-ratio analysis on T1-weighted two-point Dixon, with T2*-corrected multi-echo Dixon and T2-corrected multi-echo single-voxel MR spectroscopy sequence (MRS) for evaluation of liver fat and iron at 3T.
Methods and materials
This retrospective, HIPAA-compliant, IRB-approved study included 479 patients with known or suspected liver disease. Two-point Dixon, multi-echo Dixon, and MR spectroscopy sequences were performed for each patient at 3T. A receiver-operating characteristic analysis was performed to compare the diagnostic performance in 80 patients using biopsy as the standard. Sensitivity, specificity, PPV, and NPV of qualitative two-point Dixon results, multi-echo Dixon (PDFF and R2*), and MRS (fat fraction and R2 water) for detection of hepatic steatosis and siderosis were assessed.
Results
Fat fractions obtained from MRS and multi-echo Dixon have equivalent accuracy for detection of hepatic steatosis (AUC, sensitivity and specificity: 0.90 vs 0.88, 0.77 vs. 0.82, and 0.90 vs. 0.82), but the optimal cutoff value is higher for MRS (6.05% vs. 3.4%). The dual-ratio Dixon discrimination technique showed high negative predictive value for detection of hepatic steatosis and siderosis (0.90 and 0.94, respectively). R2* from multi-echo Dixon and R2water from MRS have equivalent accuracy for detection of iron overload at 3T (AUC 0.89 vs. 0.88). The optimal cutoff for R2* and R2water are 60.5 s−1 and 40.85 s−1, respectively.
Conclusion
The computer-aided dual-ratio discrimination with two-point Dixon is a useful qualitative screening tool with high negative predictive value for hepatic steatosis and iron overload. Multi-echo Dixon and MRS have similar accuracy for detection of hepatic steatosis and iron overload at 3 Tesla.