Erschienen in:
17.09.2015 | Magnetic Resonance
Effect of gadolinium on hepatic fat quantification using multi-echo reconstruction technique with T2* correction and estimation
verfasst von:
Mingmei Ge, Jing Zhang, Bing Wu, Zhiqin Liu, Hai Song, Xiangfeng Meng, Xinhuai Wu
Erschienen in:
European Radiology
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Ausgabe 6/2016
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Abstract
Objectives
To determine whether hepatic fat quantification is affected by administration of gadolinium using a multiecho reconstruction technique with T2* correction and estimation.
Methods
Forty-eight patients underwent the investigational sequence for hepatic fat quantification at 3.0T MRI once before and twice after administration of gadopentetate dimeglumine (0.1 mmol/kg). A one-way repeated-measures analysis of variance with pairwise comparisons was conducted to evaluate the systematic bias of fat fraction (FF) and R2* measurements between three acquisitions. Bland-Altman plots were used to assess the agreements between pre- and post-contrast FF measurements in the liver. A P value <0.05 indicated statistically significant difference.
Results
FF measurements of liver, spleen and spine revealed no significant systematic bias between the three measurements (P > 0.05 for all). Good agreements (95 % confidence interval) of FF measurements were demonstrated between pre-contrast and post-contrast1 (−0.49 %, 0.52 %) and post-contrast2 (−0.83 %, 0.77 %). R2* increased in liver and spleen (P = 0.039, P = 0.01) after administration of gadolinium.
Conclusions
Although under the impact of an increased R2* in liver and spleen post-contrast, the investigational sequence can still obtain stable fat quantification. Therefore, it could be applied post-contrast to substantially increase the efficiency of MR examination and also provide a backup for the occasional failure of FF measurements pre-contrast.
Key Points
• Fat quantification with IDEAL-based investigational sequence remains stable after gadolinium administration.
• It can be integrated into tri-phase liver MRI without adding scan time.
• This helps optimize MR protocols and provides more useful information for clinicians.