Meningioma is the most common and most diversified primary intracranial neoplasm of the CNS. The differentiation between meningioma types has implications in preoperative planning but is seldom achieved by conventional MRI. Whereas most MR parameters are intimately related to the microscopic structure of tissue, the relation is mostly a convoluted one. Lacking the identification of a single MR parameter able to fully characterize tissue microscopy, we investigate a multiparameter quantitative characterization of meningioma in clinically acceptable measurement times characterizing each tumour by its “qMRI fingerprint”.Six patients (4 female, 2 male, aged 62±15 years, from 43 to 78) selected from an on-going study, with preliminary diagnosis meningioma were scanned before surgery. Tumour grading based on histology and surgical evaluation of tissue softness are presented in Table I. MRI was performed on a Siemens 3T Prisma scanner with 80mT/m gradients and 200T/m/s slew rate. Body coil radiofrequency transmit and a 20-channel head array coil for signal reception were used. The qMRI protocol comprised a whole-brain “water mapping scan” (2D multi-slice multiple echo GRE, TR=5s, flip=25deg, 1x1x1mm, 0.75mm slice gap, 12 echoes, TE1=3.47ms, DTE=3.68ms, TA= 5mins) to which B1+ mapping was added using 4 EPI scans with 20s TR and flip angles of 30, 60, 90 and 120deg as well as a 3D T1-weighted scan (TR=38ms, flip=25deg, 12 echoes, TE1=1.48ms, DTE=3.08ms, 1x1.5x1.5 mm3, TA=4.5mins) for T1 mapping. Magnitude and phase data were saved and processed off-line. Diffusion kurtosis information for the tumour region was acquired using the double-refocused spin-echo EPI diffusion sequence, with one reference b=0 image, two acquisitions with b=1000 and 2500mm2/s using 30 diffusion directions each (other parameters: TR=6s, TE=86ms, matrix 110x110x35, 2x2x2 mm3; 1.5mm slice gap, TA=6mins). Postprocessing of the 2D-GRE data for water content was performed as described in [1]. T1 mapping was based on the comparison between the multi-echo signal obtained at long TR=5s and short TR=38ms both with flip angle of 25deg. The effective flip angle in each voxel was calculated based on a 4-angle EPI acquisition and predicted sinusoidal behaviour of the signal. Water content/relaxometry processing was performed with in-house Matlab scripts. Diffusion data were processed using ExploreDTI [2]; output included mean diffusivity, fractional anisotropy, mean kurtosis and maps based on biophysical modelling of kurtosis data such as tortuosity and axonal water fraction [3]. ROIs defining the tumour region were delineated manually based on the multiparametric information.Maps of the various quantitative parameters are presented in Fig. 1 for a patient with meningothelial meningioma (WHO I). They all reflect the presence of tumour and associated oedema. Their mean value and standard deviation over the tumour are listed in Table I together with the mean value for normal appearing WM averaged over all patients. Based on the multiparametric information, Fig. 2 shows a ‘fingerprint’ characterizing each individual tumour.The T1 and mean diffusivity values listed here are in good agreement with literature values [6,7], mean kurtosis values were higher than for both glioblastomas and low-grade gliomas [8]. With the exception of meningioma #1, tumour T1 and T2* values were higher than those of normal WM tissue. The same holds for diffusivity whereas kurtosis was substantially lower in most cases, to a large extent reflecting oedema. Water content of meningiomas, reported here in a systematic study for the first time, was markedly dichotomous. In healthy WM and GM tissue the mean water content values (70 and 83%, respectively) are constant to within a few percent values over the whole population [4]. In contrast, the mean water content of meningiomas ranged over nearly 10%. Even though this dichotomy did not correlate well with histological findings in our small study, water mapping may serve as an additional tool for the differentiation of meningioma types since it reflects fundamental properties of tissue. The correlation between water content and T1 reflects (under simplifying assumptions) the bound water fraction and its relaxation properties [5]; this also showed high variability. The correlation between water content and different diffusion parameters can also expected to be characteristic of tumour tissue and associated oedema [6] and is currently under investigation. We mention that, from the same data set, phase-based quantities such as magnetic susceptibility and electrical conductivity can be derived and will be used as additional independent parameters for tissue characterisation (e.g. calcification, Na/ion content). In conclusion, the specific fingerprint of meningiomas in a multiparametric qMRI space appears to reflect their histologic heterogeneity; a larger sample size is required to elucidate this correlation. Mean kurtosis was higher than for gliomas [8].