DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility

Abstract

Purpose: To evaluate the effect of different methods to convert magnetic resonance (MR) signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model-free and modeled hepatic perfusion parameters measured with dynamic contrast-enhanced (DCE)-MRI.

Materials and methods: In this Institutional Review Board (IRB)-approved prospective study, 23 DCE-MRI examinations of the liver were performed on 17 patients. SI was converted to [Gd] using linearity vs. nonlinearity assumptions (using spoiled gradient recalled echo [SPGR] signal equations). The [Gd] vs. time curves were analyzed using model-free parameters and a dual-input single compartment model. Perfusion parameters obtained with the two conversion methods were compared using paired Wilcoxon test. Test-retest and interobserver reproducibility of perfusion parameters were assessed in six patients.

Results: There were significant differences between the two conversion methods for the following parameters: AUC60 (area under the curve at 60 s, P < 0.001), peak gadolinium concentration (Cpeak, P < 0.001), upslope (P < 0.001), Fp (portal flow, P = 0.04), total hepatic flow (Ft, P = 0.007), and MTT (mean transit time, P < 0.001). Our preliminary results showed acceptable to good reproducibility for all model-free parameters for both methods (mean coefficient of variation [CV] range, 11.87-23.7%), except for upslope (CV = 37%). Among modeled parameters, DV (distribution volume) had CV <22% with both methods, PV and MTT showed CV <21% and <29% using SPGR equations, respectively. Other modeled parameters had CV >30% with both methods.

Conclusion: Linearity assumption is acceptable for quantification of model-free hepatic perfusion parameters while the use of SPGR equations and T1 mapping may be recommended for the quantification of modeled hepatic perfusion parameters.

Keywords: fibrosis; liver; perfusion quantification.

© 2013 Wiley Periodicals, Inc.

Figures

Figure 1

64 year-old man with chronic HCV. Liver T1 map obtained with breath-hold Look-Locker sequence (see parameters in Table 1). T1 value in right hepatic lobe was 510.6 msec.

Figure 2

64 year old man with chronic HCV (same as in Fig. 1). Select images (16.2, 24.3 and 32.4 sec after gadolinium contrast injection) shown from DCE-MRI acquisition (temporal resolution of 2.7 s, other parameters listed in Table 1) show ROI placement for perfusion processing over the abdominal aorta (in red), portal vein (PV in blue) and liver parenchyma (in green).

Figure 3

64 year old man with chronic HCV (same as in Fig. 1, 2). Signal intensity (SI) versus time curves for abdominal aorta (in red), portal vein (in blue) and liver parenchyma (in green) shown on top. Concentration vs. time curves for the aorta, portal vein and liver parenchyma using linear and SPGR conversion methods shown on the bottom. Both the SI and [Gd] (Gadolinium concentration) curves appear as expected. The arterial curves portray a sharp first pass peak with secondary recirculation peaks. Aortic signal saturation is observed on the linear conversion curve, with clear differences in gadolinium concentrations ([Gd]) seen in the 3 tissues of interest (aorta, portal vein and liver) when comparing the two conversion methods.

Figure 4

Phantom data using the IRTSE and Look-Locker sequences for measuring pre-contrast T1. R1 (1/T1 in sec−1) vs. concentration for the Look-Locker sequence correlated well with the IRTSE sequence in a concentration range of 0 to 5 mM. This corresponded to T1 values up to 676.1 msec, which fell in the range of calculated in vivo hepatic T1 values.