896 Fig. 3 An example of the effects of microperfusion on DWI measurements. To illus- trate the effects of microperfu- sion on the mono- and bi- exponential model, blood ves- sels were included in the ROI (left) which showed a large influence of microperfusion on the ADC. Then the same ROI was moved slightly until the blood vessels were excluded from the ROI (right) which showed a decreased ADC, how- ever Dslow was similar to when blood vessels were included in the ROI. The noise was defined as the average signal measured in air (green ROI), away from any visible artefacts on microperfusion, especially when the DWI sequence contains several b-values in the microperfusion range [14]. This is why in some studies researchers tried to choose b- values not within the microperfusion range [18–23]. For example, Perman et al. observed that the ADC value for liver decreased from 1.36 to 0.98 × 10−3 mm2/s when the b = 0 value was omitted; this is similar to the range of Dslow that we found. In a recent consensus report on DWI, it was recommended to use two b-values (>100 and between 500 and 1000 mm2/s) for ADC assessments [34]. It is, however, difficult to choose b-values such that microperfusion contamination of the ADC is avoided, because the micro- perfusion effects of the tissue are not known beforehand. We demonstrated in our simulation study that, although low b-values were excluded, the ADC of liver tissue was to a large extent contaminated by microperfusion and that this Table 4 Reviewed data simulated with mono-exponential analysis Reviewed data Ref. Eur Radiol (2012) 22:891–899 resulted in apparent differences of the diffusion between liver regions. The simulation study also showed that the variety of ADC values of healthy liver tissue published in the literature, were probably caused by the choice of b- values, and the mono-exponential model itself. This is a pitfall of using the ADC as a measure for molecular diffusion; there is no optimal combination of b-values, because the amount of microperfusion determines the optimal sequence of b-values and this is not known a priori. Recently, a similar simulation study was performed in the kidneys; they also found that the variability of the ADC in the kidneys is caused by the use of the mono- exponential model [35]. The microperfusion component Dfast of the bi- exponential model significantly depends on the location of the measurement. This contradicts with the results of Simulated on our data n b-values (s/mm2) #regions ADC (range) P value b-values (s/mm2) ADC (range) Nasu et al. [25] 30 0, 500 Yoshikawa et al. [30] 45 0, 600 Kiliçkesmez et al. [26] 50 0, 500 ,600 Bruegel et al. [21] 90 50, 300, 600 Mürtz et al. [19] 36 50, 300, 700, 1000, 1300 2 4 4 4 3 1.98–2.69 1.55–1.63 1.34–1.77 1.12–1.44 1.00–1.16 <0.001 0, 500 1.64–2.39 <0.05† 0, 750 1.38–1.91 <0.01 0, 500, 750 1.42–1.98 <0.001 50, 250, 750 1.02–1.41 <0.05† 50, 250, 750, 1000 0.94–1.10 (only RL)a ADCs are means in units of 10−3 mm2 /s. P values (one-way ANOVA) indicate the significance of differences between different regions in the liver. † P value was not mentioned; however the significance level was 5%. a Mürtz et al. [19] did not evaluate regions in the left lobe; therefore we excluded ROI 3 (left lobe) from the simulation P value <0.001 <0.001 <0.001 <0.001 0.001