Eur Radiol (2012) 22:891–899 Table 5 Reviewed data simu- lated with bi-exponential analysis Data are means. P values indi- cate the significance of differ- ences between different regions in the liver. aOne-way ANOVA. bNon-parametric Kruskal–Wallis N b-values (s/mm2) #regions ffast (%) Dfast (×10−3 mm2/s) fslow (%) Dslow (×10−3 mm2/s) Reviewed data [13] 25 0, 10, 20, 30, 50, 80, 100, 200, 400, 800 2 Mean (range) 26–31 71.0–85.1 69–74 1.02–1.16 P value 0.07 0.1 0.07 0.5 Simulated on our data 19 0, 50, 100, 250, 500, 750 5 Mean (range) 26– 43 27.9–57.2 57–74 0.98–1.18 897 P value 0.000b 0.001b 0.000b 0.105a Luciani et al., who did not find a location dependency of Dfast, possibly because they compared just two regions (left and right lobe) [13]. The range (71.0–85.1 × 10−3 mm2/s) was higher than what we found (27.9–57.2 ×10−3 mm2/s) when we simulated their results using our data and similar b-values. This may reflect the use of different calculation methods. In our study f, Dfast and Dslow of an ROI were calculated by taking the medians of the underlying b-maps as an input for the fitting procedure, where Luciani et al. first calculated Dfast, Dslow and f maps on a pixel-by-pixel based fitting procedure, after which the average of an ROI was calculated. In addition, they used six b-values under 100 s/mm2 (versus two in our study), which tends to decrease the uncertainty of the fitting algorithm, and increase the slope of the curve close to zero. This might explain the higher Dfast and the decreased range of Dfast in their study. However, we have shown that even with seven (rather than 16 b-values) the bi-exponential fits are already extremely accurate, which is supported by the nearly identical values of Dslow in their study and our simulation study. Too little b-values under b =100 s/mm2 can however hamper an accurate determination of Dfast, especially when analysing tissues with high Dfast values, which can be expected in pathology or near blood vessels. The increase in the ADC in the left lobe, which was demonstrated in Fig. 2, is usually explained from the increased cardiac motion in the left lobe [19, 25, 36–38]. However, we found that the fraction of microperfusion ffast in the left lobe was almost a factor two higher than in other liver locations. Although pseudo-anisotropy is a known artefact of respiratory triggering in the liver [39], we suspect that the increased ADC in the left lobe may be caused by extensive microperfusion contamination of the ADC and to a lesser extent by either cardiac or respiratory artefacts. This is supported by a study on the hepatic perfusion of eight hepatic segments by dual-source com- puted tomography [40]. They found that the hepatic perfusion index (HPI) was significantly higher in segment 3 (left lobe) than in segments 5 to 8 (right lobe), and suggested that this might be related to the anatomy of the liver vessels. Another study using pharmacokinetic analysis of dynamic contrast-enhanced MRI demonstrated that regional variations in liver microcirculation can be dis- played by colour-coded parameter maps [41]. They found a minor variation of perfusion in an apical section of a transplanted liver. The left part of the liver, corresponding to segment 2, showed a different perfusion rate than the right part of the liver. In an experimental study on rats using perfusion CT, the relative blood flow in the left lobe was 17% higher than in the right lobe of the liver [42]. We suspect that these regional variations in the density of small blood vessels and capillaries caused the heterogeneous appearance of microperfusion throughout the liver. This is supported by our analysis of including a blood vessel in the ROI, which resulted in more microperfusion contamination than when blood vessels were avoided. Some studies have reported on age-related changes in liver structure and function [43, 44]. However, Pasquinelli et al. showed no significant variations in liver DWI quantitative parameters according to the age of the subject [45]. Therefore, we assumed that the possible effects of age in our cohort are far smaller than the demonstrated significant region dependency of the ADC in the liver. Although the conclusions in this study are drawn from healthy volunteers, we suspect that the effects of micro- perfusion are much larger in pathology, and should therefore be applicable to patient data also. In conclusion, the ADC of normal liver tissue is significantly dependent on the measurement location because of substantial microperfusion contamination; there- fore the bi-exponential model should be used. Currently, the diagnostic use of DWI for discriminating hepatic masses, liver cirrhosis and fibrosis is mainly based on the ADC, and the discrimination between diseased and healthy tissue may therefore be hampered [6–8, 15–18, 20, 21, 23, 30, 37, 46]. Thus, the bi-exponential model is essential for the future development of the clinical diagnostic application of DWI in the liver.