Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

Journal Pre-proof Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

Marco Andrea Zampini, Giulia Buizza, Chiara Paganelli, Giulia Fontana, Emma D’Ippolito, Francesca Valvo, Lorenzo Preda, Guido Baroni PII:

S0730-725X(19)30350-9

DOI:

https://doi.org/10.1016/j.mri.2019.12.003

Reference:

MRI 9353

To appear in:

Magnetic Resonance Imaging

Received date:

2 June 2019

Revised date:

15 November 2019

Accepted date:

5 December 2019

Please cite this article as: M. Andrea Zampini, G. Buizza, C. Paganelli, et al., Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI, Magnetic Resonance Imaging(2019), https://doi.org/10.1016/j.mri.2019.12.003

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof

Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

of

Marco Andrea Zampinia,b,∗, Giulia Buizzaa , Chiara Paganellia , Giulia Fontanac , Emma D’Ippolitoc , Francesca Valvoc , Lorenzo Predac,d , Guido Baronia,c a

lP

re

-p

ro

Politecnico di Milano, CartCasLab, Department of Electronics Information and Bioengineering, Piazza Leonardo Da Vinci 42, Milan, Italy b MR Solutions Ltd, Ashbourne House, Old Portsmouth Rd, Guildford, United Kingdom c Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, Pavia, Italy d Universit` a di Pavia, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, Via Alessandro Brambilla 74, Pavia, Italy

Abstract

Jo

ur

na

Multiparametric MRI is a remarkable imaging method for the assessment of patho-physiological processes. In particular, brain tumor characterization has taken advantage of the development of advanced techniques such as diffusion- (DWI) and perfusion- (PWI) weighted imaging, but a thorough analysis of meningiomas is still lacking despite the variety of computational methods proposed. We compute perfusion and diffusion parametric maps relying on a welldefined methodological workflow, investigating possible correlations between pure and diffusion-based perfusion parameters in a cohort of 26 patients before proton therapy. A preliminary investigation of meningioma staging biomarkers based on IntraVoxel Incoherent Motion and Dynamic Susceptibility Contrast is also reported. We observed significant differences between the gross target volume and the normal appearing white matter for every in∗

Corresponding author: Marco Andrea Zampini Maling address: MR Solutions Ltd, Ashbourne House, Old Portsmouth Rd, Guildford, GU3 1LR, United Kingdom Email address: [email protected] (Marco Andrea Zampini) Preprint submitted to Magnetic Resonance Imaging

December 12, 2019

Journal Pre-proof

vestigated parameter, confirming the higher vascularization of the neoplastic tissue. DWI and PWI parameters appeared to be weakly correlated and we found that diffusion parameters – the perfusion fraction in particular – could be promising biomarkers for tumor staging.

of

Keywords: Multiparametric MRI, Meningioma, Diffusion MRI, IntraVoxel Incoherent Motion (IVIM), Perfusion MRI, Dynamic Susceptibility Contrast (DSC)

ro

1. Introduction

Jo

ur

na

lP

re

-p

Over the last few decades, the use of Magnetic Resonance Imaging (MRI) has become crucial in tumor characterization [1, 2]. In the subsequent definition of tailored treatments, a multiparametric approach in MRI offers the possibility of assessing multiple facets of coexisting pathophysiological processes in a non-invasive way [3], thereby further assisting in patient treatment management and longitudinal analyses. A number of advanced MRI techniques have been specifically developed for the assessment of Central Nervous System (CNS) – and in particular brain – tumors [4]. Among these techniques, perfusion weighted MR imaging (PWI) and diffusion-weighted MR imaging (DWI) are recognized as an important means to outline tumors and are gaining acceptance as imaging biomarkers for tumor detection, characterization and monitoring [5]. Since the malignancy potential of a brain tumor is related, among other factors, to the degree of angiogenesis [6] and thus to blood supply, brain PWI has been investigated [7, 8]. This fact supports the quantitative assessment of microcirculation in brain tumors, among which meningioma is the most frequently diagnosed, accounting for the 37% of the overall primary CNS tumors diagnosed in the United States between 2011 and 2015 [9]. Specifically, Dynamic Susceptibility Contrast (DSC, also known as bolus tracking MRI ) in PWI can provide information on brain tumor vascularization that may help in making differential diagnosis [10]: DSC allows for the estimation of multiple perfusion parameters (customarily normalized against the normal appearing white matter) relying on the passage of an exogenous intravascular tracer that produces a transient signal loss due to susceptibility effects [11]. Although DSC can provide insights on novel imaging biomarkers [12], the lack of consensus on computational methods [13, 14] and unifying terminology of quantitative perfusion parameters limits a thorough investigation 2

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

of DSC in brain tumors as meningiomas. Similarly to PWI, DWI is important in neuro-oncology, allowing the quantification of changes in the movement of water molecules that occurs in tumors, thus describing their microstructure and heterogeneity. If low b-values (b ≤ 200 s/mm2 in brain [15]) are acquired during a DWI protocol, it is possible to disentangle the pure diffusion from the perfusion phenomenon, which can be seen as pseudo-diffusion. The proposed relationship between classical perfusion parameters (i.e. derived from DSC) and those estimated through the IntraVoxel Incoherent Motion (IVIM) model [16, 17] incorporates capillary network geometry and microcirculation quantities as proportionality factors. Currently, a clinical verification of these relationships has been performed in gliomas, with contrasting results [18, 19], but to the best of our knowledge, no investigation of this type has been performed on meningiomas. All the above considerations are relevant when deriving imaging biomarkers for supporting clinical decisions in treatments that require an accurate tumor characterization, such as particle therapy. Particle beams based on protons or heavier ions allow indeed better dose deposition with fewer adverse effects when compared with conventional radiotherapy [20]. Proton therapy has been used to treat meningiomas since the early 1980s, albeit with nonmodern technology, imaging, and planning tools which, nevertheless, have accrued long-term follow-up, showing a high 5-year recurrence-free and overall survival [21]. A better definition of meningioma features relying on multiparametric MRI could potentially allow patient-specific treatment optimization with improved clinical decision-making and dose-escalation strategies. The aim of the present study consists in the estimation of DSC and IVIM parameters for meningioma patients undergoing proton therapy, by means of a multiparametric-MRI approach. First, perfusion and diffusion parameters are computed relying on a well-defined methodological workflow, and possible correlations between pure and diffusion-based perfusion parameters are investigated. Then, a preliminary characterization of such tumors through quantitative perfusion and diffusion parameters is performed by comparing two groups of histologically-verified high and low grade meningiomas. 2. Materials and Methods 2.1. Imaging protocol A total of 26 patients with confirmed diagnosis of meningioma were retrospectively selected and divided into two groups based on tumor grade – 3

Journal Pre-proof

-p

ro

of

WHO I considered as low grade, WHO II and III considered as high grade. While the tumor histology was known for 18 patients (12 high grade, 6 low grade), no biopsy was performed on 8 patients, for which no grade was provided. Informed consent was collected prior to the first imaging session. The patients underwent proton therapy at Centro Nazionale di Adroterapia Oncologica (CNAO, Pavia, Italy) and were scanned with a clinical MR scanner (Siemens MAGNETOM Verio 3T, Siemens Healthineers, Erlangen, Germany) using a 32-channel head coil ahead of treatment. Standard anatomical 3D T1 - (VIBE, TE/TR = 2.49/5.35 ms, α = 11◦ , resolution = 0.6 x 0.6 x 0.6 mm) and 2D T2 -weighted (TSE, TE/TR = 74/12960 ms, flip angle α = 150◦ , resolution = 0.75 x 0.75 x 2 mm) MR images were acquired together with DWI and PWI sequences.

Jo

ur

na

lP

re

• DWI protocol. DWI trace images were acquired with a Twice Refocused Spin Echo - Echo Planar Imaging (TRSE-EPI) pulse sequence. A set of 7 b-values (0, 50, 100, 150, 200, 400, and 1000 s/mm2 ) in the 3 averaged orthogonal directions was acquired (TE/TR = 76/600 ms, α = 90◦ , resolution = 0.94 x 0.94 x 4 mm, interslice gap = 0.8 mm, matrix size = 320 x 264 pixels). The definition of the number of bvalues satisfied recommendations [22] reported for the computation of a simplified IVIM model. Specifically, the best time-quality trade-off was reached by acquiring scans with only two b-values (400 and 1000 s/mm2 ) in the b > 200 s/mm2 regime [22, 15], whereas b-values in the (0,50] s/mm2 interval were not achievable due to scanner limitations. The sequence acquisition time ranged between 3.5 and 6.5 minutes (acquisition time of 1.8 s/(slice x b-value)). • PWI protocol. A Gadolinium-based contrast agent among Gd-BOPTA (Gadobenate dimeglumine, MultiHance, Bracco, Milan, Italy), GdDO3A-butrol (Gadobutrol, Gadovist, Schering-Plough, Kenilworth, New Jersey, USA) and Gd-HP-DO3A (Gadoteridol, ProHance, Bracco, Milan, Italy) was injected in an antedecubital vein through an automatic MRI-compatible power injector: prior to PWI scans, a 2 mL volume of contrast pre-bolus was injected in order to compensate for contrast agent leakage due to Blood-Brain Barrier extravasation [23], which could otherwise alter perfusion maps and their interpretation. When comparing the effects of using Gadovist or MultiHance in DSCMRI [24], no pronounced difference between the agents was found, if 4

Journal Pre-proof

-p

ro

of

provided at identical doses. Based on these observations, we extended the consideration also to the use of ProHance. In order to maintain similar perfusion bolus profiles, injection rates (mL/s) for Prohance and Multihance were derived as patient weight [kg] /5, while for Gadovist it corresponds to patient weight [kg] /10. Then, axial T∗2 -weighted images were acquired using a Gradient Echo - Echo Planar Imaging (GRE-EPI) sequence (TE/TR = 30/1800 ms, resolution = 1.8 x 1.8 x 4 mm, slice thickness = 4 mm, interslice gap = 1.2 mm, α = 90◦ , number of slices = 24, FOV = 230 mm, matrix size = 128 x 128 pixels). The major dose (standardized for patient weight) of Gadolinium contrast agent was injected during the 5th-6th slice acquisition of the whole volume; 60 scans per slice were acquired with a time step of 0.905 s giving a total amount of 1440 perfusion-weighted images acquired per patient.

Jo

ur

na

lP

re

Previously delineated CT-based Gross Tumor Volume (GTV) contours, T2 -weighted based Normal Appearing White Matter (NAWM) contour and diffusion-weighted images were rigidly registered on the second PWI volume acquired (to ignore saturation effects [25]) through Plastimatch [26], with a 2-stage approach and using mutual information as merit function. A well-defined and automatic computation procedure of the PWI and DWI maps was developed and adopted. To clear up any confusion generated by the lack of specificity in maps processing, as too often reported in scientific literature, a detailed step-by-step description of the method followed here is available in Appendix. A synthetic scheme of the maps computation workflow is also reported in Figure 3a. 2.2. DSC maps computation The computation of the arterial blood signal Ca , also called Arterial Input Function (AIF), is a major source of variability in the computation of perfusion maps [27, 28, 29] as it deeply affects them. A global AIF – widely used in DSC MRI analyses – was estimated through a clustering approach. Candidate voxels were selected on the rigidly-registered ADC maps as hyperintense ones, pertaining to the Willis Circle and Middle Cerebral Artery. Then, arterial features, such as an early bolus arrival, a steep rise, a narrow peak, an early time-to-peak, an early first moment of the curve, a large integral and a high maximum peak concentration – all characteristics associated with arterial blood signals – were computed from the candidate voxels and 5

Journal Pre-proof

tj

lP

Z

re

-p

ro

of

a k-means clustering (k = 4, empirically tuned) was performed. Different curve types were clustered based on the Euclidean distance (which is higher for curves belonging to different types than for curves belonging to the same type/cluster) and 4 groups of curves internally exhibiting common shape features were identified. Due to sensitiviy of k-means clustering to the randomness of the starting points, the final cluster allocation for each curve is neither unique nor optimal [7]. Therefore, curves were iteratively regrouped until cluster assignments did not change and up to a maximum number of iterations of 100. Such method aimed at providing a faster, more reproducible and objective AIF computation method than manual approaches. A model-independent technique was adopted and the estimation of Cerebral Blood Volume (CBV) maps relied on the ratio of the area between the voxel-wise tissue concentration curve and the patient-specific AIF. Extensively used in the analysis of tracer transport functions [30, 31], a linear algebraic approach has been chosen for deconvolution in this work, where the tissue concentration curve is given by Ca (τ )R(tj − τ ) dτ ≈ CBF · ∆t

Ct (tj ) = CBF 0

j X

Ca (ti )R(tj − ti ) , (1)

i=0

Jo

ur

na

where Ct is the tissue contrast agent concentration, CBF the Cerebral Blood Flow, Ca the arterial blood signal and R the residue function. Specifically, in order to estimate CBF maps, Single Value Decomposition was applied as it is independent upon microvascular hemodynamics and can provide precise perfusion estimates in clinical EPI measurements [32]. As CBF corresponds to the peak value of the tissue impulse response function, Mean Transit Time (MTT) can then be computed exploiting the Central Volume Theorem. For further details, a step-by-step algorithm is attached in Appendix. The tissue concentration signal from the NAWM volume – segmented on T2 - and registered to the T∗2 -weighted images – was used to normalize the PWI maps (relative maps are hereon referred to as rCBV, rCBF and rMTT). 2.3. Diffusion-Weighted Imaging and IVIM maps computation Diffusion weighted images provide different parameters according to the underlying diffusion model that is hypothesized: mono-exponential and biexponential models can provide ADC and IVIM parameters, respectively. The IVIM model can be represented by a double-exponential model for the

6

Journal Pre-proof

voxel-wise signal intensity S: S(b) = S0 {f · exp [−b(D∗ )] + (1 − f ) exp (−bD)}

,

(2)

Jo

ur

na

lP

re

-p

ro

of

where D∗ is the pseudo-diffusion coefficient, f is the perfusion (flowing) fraction and D is the true diffusion coefficient, i.e. the diffusion coefficient estimated without perfusion effects. While D∗ and f pertain to a perfusion phenomenon description more related to low b-values (b < 200-250 s/mm2 ), D is a diffusion-related parameter: jointly with the ADC, all the parameters have a geometrical interpretation in the log(S/S0 )-b plane and, as such, they represents the coefficients of regression fit models. Parametric maps of D∗ , D and f (IVIM maps) were computed voxel-wise by fitting the double-exponential model in Eq. (A.10) to 7 diffusion intensity data values from TRSE-EPI images, using a bounded, trust-region, non-linear least squares fit. In order to yield only patho-physiologically meaningful results, optimal solutions for the IVIM parameters were constrained within specific intervals, as previously suggested [33]: f ∈ [0, 35] %, D ∈ [0, 0.01] mm2 /s, D∗ ∈ [0, 0.1] mm2 /s. Literature values for both NAWM and gray matter are found well within these ranges [34], and values for meningiomas are expected not to exceed the provided ranges: a recent study [35] reports values for grade I and II meningioma to be 32.4 ± 7.0 % and 28.8 ± 2.5 % for f, 0.52 ± 0.03 10−3 mm2 /s and 0.46 ± 0.07 10−3 mm2 /s for D, 3.71 ± 1.77 10−3 mm2 /s and 5.17 ± 2.57 10−3 mm2 /s for D∗ , respectively. Also, ADC maps were computed on-scanner through a linear regression over all the 7 data-points. Both diffusion (ADC, D) and perfusion IVIM (D∗ , f) related maps were estimated for each patient, and they will be here jointly referred as DWI maps. 2.4. Experiments To evaluate the derived PWI and DWI quantitative maps, Spearman correlation among median values was computed over the whole population, for both NAWM and GTV. All the analyses were performed on the meningioma GTV and, for comparison and evaluation of the proposed procedure, in the NAWM. The relationship between PWI and DWI maps according to Le Bihan and Turner [16, 17] was also investigated in both NAWM and the meningioma GTV. Additionally, for tumor characterization, a histogram analysis of both relative PWI (CBV, CBF, MTT) and DWI (ADC, D, D∗ , f) maps in the GTV 7

Journal Pre-proof

-p

ro

of

was performed in the two patients groups separately. Analogously, absolute PWI and DWI maps were analyzed in the NAWM. In the majority of the cases, normality of the data was rejected by a Lilliefors test, so univariate analysis was performed via a Wilcoxon rank-sum test (Mann-Whitney U test) to test distribution differences between the two patient groups for all the aforementioned PWI and DWI parameters. The level of significance was set at α = 0.05 throughout the statistical analysis. Logistic regression was used to fit models based on either single or combinations of parameters significantly differentiating between the low and high grade meningioma groups. The binomial classification provided as outcome of the logistic model was used to compute Receiver Operating Characteristic (ROC) curves with bootstrapping and classical metrics for the evaluation of these curves were reported.

re

3. Results

Jo

ur

na

lP

3.1. PWI/DWI correlation All 26 patients were successfully scanned and were included in the analysis of PWI and DWI maps. An example of the PWI and DWI maps is provided in Figure 1, together with the respective high resolution T1 - and T2 -weighted images. The distribution of the median residuals sum of squares for DWI maps (adimensional values defined in terms of normalized diffusion signal S(b)/S(b0 )) was analyzed to provide a fitting accuracy metric: median [interquartile range] values were 3.28 [2.86-3.96] x10−4 for NAWM and 8.10 [6.06-19.38] x10−4 for GTV, thus providing reliable IVIM maps. Spearman correlation between median values of PWI and DWI parameters in the whole brain was computed on the entire patient cohort. Following Evans’ guidelines for correlations [36], a very strong correlation (0.8 ≤ rho < 1) was found between CBF and CBV, while a strong correlation (0.6 ≤ rho < 0.8) was found between the pairs D-D∗ , f-D and f-D∗ . A graphical interpretation of the median values distribution for both GTV and NAWM is reported in Figure 2, where absolute and relative scales are given for the PWI maps, whereas Figure 3b shows the correlation matrix between the median values of all the considered parameters. Significant differences for all parameters are found between NAWM and GTV, with NAWM showing lower values for both the perfusion- and the diffusion-related maps.

8

Journal Pre-proof

Eventually, the voxel-wise correlation between PWI and DWI maps linked through microstructural and microvasculature proportionality factors, as proposed by LeBihan et al. [16, 17], was tested. The correlation of the median values resulted weak (0.2 ≤ rho < 0.4) in all the considered pairs (CBV and f, CBF and fD∗ , MTT and D*), and for both GTV and NAWM: specifically, CBV and f parameters had a correlation of 0.16/0.09, CBF and fD* of 0.32/0.30, while MTT and D∗ of 0.06/-0.35 for GTV/NAWM.

Jo

ur

na

lP

re

-p

ro

of

3.2. Meningioma characterization 3.2.1. Histogram analysis 18 patients with known tumor histology (12 high grade, 6 low grade) were included in the tumor characterization analysis: as the position (mean, median, quartiles), variability (standard deviation), and shape (skewness, kurtosis) statistics for each parameter have a distribution within the two patients groups, the median of such distributions are reported in Tables 1 and 2. The Wilcoxon rank-sum test showed no statistical differences between patients with a high or a low grade tumor in terms of relative PWI for most of the parameters. A significant difference was observed for the standard deviation of rCBV, which was higher in the low grade group (high/low grade: 3.23/5.64). Median values for rCBV, rCBF and rMTT were respectively 4.28/3.37, 2.32/2.78 and 1.49/1.19 for the high/low grade group. Differences between the two groups were found instead for all the DWI parameters: median values of D∗ , as well as the mean values of ADC and D∗ , the standard deviations of ADC, 25th percentile for D and the 75th percentile for both ADC and f. All the parameters distributions are fairly symmetrical in both the groups, except for the D∗ which shows a higher skewness (0.88/2.26) and a leptokurtic (4.57/15.53) distribution; besides, skewness and kurtosis of both D∗ and D differ between the two groups. Table 6 lists rCBV values found in scientific literature regarding meningioma tumors. 3.2.2. ROC curves analysis The parameters showing a statistical difference between the low and high tumor grade groups were used to fit logistic regression models, which were evaluated through ROC curves. The 75th percentile of f achieved the highest AUC (0.96 - Youden’s J index = 0.75, accuracy = 89%), whereas median and mean D∗ – together with the 25th percentile of D – achieved the highest J index. A complete list of the ROC curves metrics can be found in Table 6 and an example of ROC curve for the 75th percentile of f is reported in 9

Journal Pre-proof

Figure 4. A three-parameter models including f, D and D∗ was the simplest model to provide an AUC = 1, while the two-parameters model including f and D provided an AUC = 0.972 with an accuracy of 83% (Figure 4). 4. Discussion

Jo

ur

na

lP

re

-p

ro

of

In this study, we report the potential of multiparametric perfusion and diffusion weighted MRI for meningioma tumors characterization. Despite recent PWI- and DWI-related characterization studies of brain tumors ([8, 13, 37, 38, 39, 40, 41]), we provide a step-by-step - thus reproducible computational algorithm for the fully automatic computation of both PWI and DWI maps. When correlating quantitative maps in terms of median values, DWI parameters were found to be strongly correlated to each other (especially f, D and D∗ ), while CBV and CBF were the most correlated parameters (rho > 0.8). The nature of such correlations, either biological or induced by the parameters estimation itself, is not known and experimental groundwork is needed to resolve such uncertainty. Besides, when comparing PWI and DWI voxel-wise through the Spearman correlation, we were not able to confirm the relationships hypothesized by Le Bihan [17]. Although the DWI protocol was defined according to considerations from the literature, the limitation of the scanner in acquiring b-values in the low range prevented a very accurate estimation of perfusion-related parameters. It should be noted, indeed, that the number of b-values available below 200 s/mm2 is lower than the amount customarily used in IVIM studies: a number of b-values frequently found in literature is 10, against the 5 values here acquired. Moreover, the IVIM-DWI signal appears to be more sensitive to incoherent flow arising in the microvasculature, while the DSC one to large vessels, thus raising concerns on the direct comparison between the two [42]. Indeed, although some groups succeeded in finding a correlation in gliomas [18, 43, 22], others did not achieve the same correlation in brain [19, 44]. To the best of our knowledge, no similar evaluation had been reported for meningiomas, and such preliminary analysis could further support that the comparison between IVIM parameters and DSC is still inconclusive and must be clarified [42]. As regards our investigations on meningioma characterization, whereas no statistical differences between high and low grade groups were found for median and mean values in relative PWI maps, differences could be observed 10

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

for DWI parameters. In particular, median D∗ (5.60/8.12 10−3 mm2 /s) was significantly different, as well as mean ADC (1.19/1.09 10−3 mm2 /s) and D (12.50/15.51 10−3 mm2 /s). Median values in the GTV differed from those in the NAWM for every parameter, confirming the higher vascularization of the neoplastic tissue. NAWM values were found comparable to literature ones [34], suggesting NAWM to be a reliable reference tissue. Although median and mean values for meningioma tumors seem to vary in the scientific literature (as reported in Table 6), the order of magnitude of the values here reported was found to be similar to literature ones, attesting the reliability of the methodology providing quantitative maps. However, a direct comparison could not be carried on due to the diversity of the computational methods employed. The definition of a general consensus still needs to be established for effective quantitative approaches to Dynamic Susceptibility Contrast [1] and IntraVoxel Incoherent Motion models applied to brain tumors, such as meningiomas. Providing an intercohort analysis of ROC curves, the preliminary results on the two patient populations (low vs. high grade meningiomas) suggest that DWI parameters - taken individually or combined in a linear model could be used as imaging biomarkers for staging: specifically, the estimated perfusion fraction f was found to be the best parameter in terms of predictive power (AUC = 0.958) to discriminate the tumor grade, while DSC perfusion parameters still need a wider investigation. When considering linear models comprising multiple DWI parameters, ROC curves still resulted in a high AUC (AUC > 0.9), though these results could be overly optimistic due to the limited sample size. Main limitations consist in the above-mentioned sparse b-values sampling scheme in the regime of b < 50 s/mm2 , along with the retrospective nature of the study and the limited patients population. Further analyses are, therefore, required on a denser and optimized b-values distribution and on a wider patient cohort, to confirm the reported findings on multiparametric MRI biomarkers, before their use in defining tailored treatments in particle therapy. Future studies are indeed planned on collecting additional data on meningioma tumors, relying on consistent imaging acquisition protocols and well-defined quantitative map computation, building on the results presented in this study.

11

Journal Pre-proof

5. Conclusion

-p

ro

of

We reported a multiparametric analysis based on diffusion and perfusion MRI of patients affected by meningioma. A well-defined methodological framework for quantitative MRI is presented and IVIM and DSC parameters are investigated as imaging biomarkers for meningioma characterization. In our preliminary analyses, diffusion parameters resulted to be promising biomarkers for tumor staging. Perfusion DSC parameters, instead, did not report a significant statistical difference, even though results overall agreed with the literature. These findings highlights the need for further developments and insights of DSC and IVIM parameters. We put forward the potential of this work for additional studies on meningioma tumor characterization, especially for applications in particle therapy treatments.

re

6. Acknowledgments and grant disclosure

Tables

na

lP

We thank Luca Anemoni for the help in data collection, Martina Guidetti and Dr. Richard L. Magin for proofreading the paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Jo

ur

Table legend Table 1: Median values for the distributions of the indices for obtained from the relative PWI maps. The bold font is used whenever a statistical difference was found (Wilcoxon rank sum test, α = 5%). Table 2: Median values for the computed indices for the relative DWI maps. The bold font is used whenever a statistical difference was found (Wilcoxon rank sum test, α = 5%). Table 3: rCBV values for meningioma in literature. WHO grades: benign (grade I), atypical (grade II), and malignant (grade III). Table 4: ROC curve analysis on the parameters providing a difference between the high and low grade groups: the AUC, as well as the specificity (Sp) and sensitivity (Se) of the cut-off point, accuracy and Youden’s J index are reported (dimensionless quantities).

12

Journal Pre-proof

PWI rCBV [-] Low

median

4.28

mean

4.06

std

rMTT [-]

High

Low

High

Low

3.37

2.32

2.78

1.49

1.19

4.44

2.31

2.98

1.76

1.67

3.23

5.64

2.44

3.35

2.12

2.45

25th perc

3.54

2.09

2.21

2.11

1.18

0.70

75th perc

5.26

5.26

2.41

3.44

1.74

1.61

0.64

0.78

0.31

0.39

0.32

0.35

0.53

0.53

kurtosis

0.13

0.12

0.31

0.28

-p

Table 1:

ro

skewness

of

High

rCBF [-]

DWI

ADC [x 10−3 mm2 /s]

D [x 10−3 mm2 /s]

High

Low

High

median

1.15

1.01

mean

1.19

1.09

std

0.54

0.46

0.61

D* [x 10−3 mm2 /s]

f [%]

High

Low

High

Low

0.82

0.71

5.60

8.12

13.4

10.1

0.82

0.78

12.50

15.51

14.6

12.1

0.71

16.81

18.57

10.4

10.3

lP

re

Low

0.80

0.82

0.51

0.28

1.81

1.91

6.60

2.30

75th perc

1.57

1.33

1.25

1.07

15.81

20.83

22.2

19.8

0.60

0.88

2.26

2.22

1.92

31.6

57.1

4.02

4.57

15.53

8.20

6.96

2.02

2.20

0.11

kurtosis

2.72

ur

skewness

na

25th perc

Jo

Reference Holveck et al. (2010) Toh et al. (2014)

[10] [45]

Hussain et al. (2017) [46] Romano et al. (2012) [37] Kremer et al. (2004) [47] Van et al. (2011) [48] Zhang et al. (2008) [5]

Table 2:

rCBV 4.6 ± 1.6 5.06 ± 2.05 low vascularity (grade 0,1) 13.9 ± 5.67 high vascularity (grade 2,3) 5.7 ± 2.2 ml benign 8.02 - 10.58 range 8.97 ± 4.34 7.90 range: 3.46 - 8.95 7.16 ± 4.08 benign (mean max value) 5.89 ± 3.86 malign (mean max value) Table 3:

13

Journal Pre-proof

AUC 0.806 0.792 0.875 0.806 0.889 0.788 0.806 0.958

cut-off Sp 0.750 0.917 1.000 0.917 0.917 0.727 0.750 0.917

cut-off Se Accuracy 0.833 0.778 0.500 0.778 0.667 0.889 0.667 0.833 0.833 0.889 1.000 0.824 1.000 0.833 0.833 0.889

of

parameter rCBV std ADC mean std 75th perc D 25th perc D* median mean f 75th perc

ro

Table 4:

J index 0.583 0.417 0.667 0.583 0.750 0.727 0.750 0.750

-p

Figures

Jo

ur

na

lP

re

Figure legend Figure 1: Violin plots [49] of the median values for GTV and NAWM (a) and Spearman correlation analysis of the median values for the relative PWI and DWI parameters (b), for the whole patient group. Violin plots show the full data distribution of a population: a smoothed probability density is showed together with the median value (white dot) and the interquartile range (thick gray line). Figure 2: T1 - and T2 -weighted images, PWI and DWI maps of a patient with a low grade meningioma on the left skull base region. The tumor is well recognizable as a hyper-intense mass in the left retro-orbital region in the T1 -weighted as well as in the CBV map. The patient-specific global AIF is also reported as a variation of transverse relaxation rate (∆R∗2 ) over time. Figure 3: (a) Workflow for the computation of the DWI and PWI maps with the main steps and interconnections. Orange boxes specify the registration steps. Further details are reported in Appendix. (b) Spearman correlation matrix between the median values of all the considered parameters in the whole patient cohort. Figure 4: ROC curve for the 75th percentile of f when used as a binary classifier. Figure 5: ROC curve for multivariate analysis (the third – cyan – curve overlaps with the “All DWI” curve). Figure 6: Biexponential signal decay as a function of the 7 b-values used in this project. After a logarithmic transformation, a least squares optimization based-fitting provides D, D∗ and f, highlighting and separating the contri14

-p

Figure 1:

ro

of

Journal Pre-proof

Jo

ur

na

lP

re

butions associated to perfusion and true diffusion. The monoexponential fit provides instead the ADC coefficient.

15

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Figure 2:

16

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Jo

CBF MTT ADC D D* f

0.85 0.44 0.15 0.25 0.28 0.13 CBV

(a)

0.06 0.07 0.06 0.32 0.19 CBF

0.29 0.30 0.05 -0.09 MTT (b)

Figure 3:

17

0.60 0.28 0.68 0.43 0.71 0.72 ADC D D*

re

-p

ro

of

Journal Pre-proof

Jo

ur

na

lP

Figure 4:

Figure 5:

18

Figure 6:

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

19

Journal Pre-proof

Appendix A. Methodological background

ur

na

lP

re

-p

ro

of

Appendix A.1. Dynamic Susceptibility Contrast DSC imaging allows bolus tracking of a rapidly injected contrast agent (Gd-based chelates) by means of T2 - (SE) or T∗2 - (GE) weighted sequences [50]. With an intact Blood-Brain Barrier, the first-pass extraction of contrast agent is null and strong microscopic susceptibility gradients are generated. Such gradients lead to spin dephasing providing signal loss in T2 - and T∗2 weighted images [11], where the signal loss is a function of TE, vessel size distribution density, contrast agent concentration and magnetic properties. GE-based measurements were found to be equally sensitive to all vessel sizes [51, 52] and this is the approach here undertaken. When an intravenous (IV) injected contrast agent reaches the tissue through the feeding artery, a fraction of contrast agent perfuses into brain tissue, thus a tissue tracer retention phenomenon needs to be quantified in order to compute perfusion maps. The central assumption of the kinetic analysis underlying DSC maps computation is the linear relationship between the transverse relaxation rate and the tissue contrast agent concentration Ct (t) [40]. This has been confirmed in in-vivo experiments [53] and is currently used extensively in perfusion measurements [54]. Accordingly, the tissue contrast agent concentration can be expressed as   S(t0 ) TE . (A.1) C(t) = k · ln S(t)

Jo

where k depends on the field strength, contrast agent, tissue type and pulse sequence [40]. Scientific literature comes with a variety of perfusion maps computation algorithms: a thorough analysis of the main methods is reported in the study by Orsingher et al. [13]. During a single bolus tracking in DSC MRI, arterial Ca (t) and tissue Ct (t) concentration time curves can be computed as in Equation A.1. When a time series of scans is acquired, a CBV map is given by the ratio of their area under the curve [55]: Z tend Ct (t) dt 0 CBV = Z tend . (A.2) Ca (t) dt 0

20

Journal Pre-proof

As an attempt to normalize CBV values across patients, the ratio between values in the tumor volume and in the Normal Appearing White Matter, namely the rCBV, has been used in neuro-oncology [56]. The so-called Residue function R is introduced, measuring the fraction of tracer within the vasculature at time t after injection [54]. As the tracer gradually perfuses within the tissue, the residue is a decreasing function of time, with R(0) = 1 and R(∞) = 0. For an impulse-like injection, giving Ca (0) = max Ca (t), the tissue concentration Ct (t) for each voxel is provided t

ro

of

by the time convolution of the tissue impulse response function (product of CBF and R(t)) with Ca : Z t1 Ct (t) = CBF · Ca (t) ~ R(t) = CBF Ca (τ )R(t − τ ) dτ . (A.3)

-p

0

ur

na

lP

re

CBF and rCBF result to be valuable parameters in perfusion MRI imaging, so their determination through the deconvolution of the tissue impulse response function in Equation A.3 has been broadly investigated [31, 54, 57]. Deconvolution represents an inverse problem, where an integral equation needs to be solved with respect to an unknown kernel, in this case R(t), which is an unknown function dependent on local vascular structure. This dependency makes deconvolution not straightforward, requiring one to fit the tissue impulse response function from the experimental data, which comes with the intrinsically ill-posed nature of deconvolution. By rewriting Equation A.3, a model-independent technique relying on a linear algebraic approach is formulated as tj

Jo

Z

Ct (tj ) = CBF

Ca (τ )R(tj − τ ) dτ ≈ CBF · ∆t

0

j X

Ca (ti )R(tj − ti ) .

i=0

(A.4)

This can be equivalently written as    Ct (t1 ) Ca (t1 ) 0  Ct (t2 )   Ca (t2 ) Ca (t1 )     ...  = CBF · ∆t  ... ... Ct (tN ) Ca (tN ) Ca (tN −1 )

   ... 0 R(t1 )   ... 0   ·  R(t2 )  (A.5) ... 0   ...  ... Ca (t1 ) R(tN )

or in a vector and matrix notation of the kind c=A·b 21

(A.6)

Journal Pre-proof

re

-p

ro

of

where A being the product of the first three terms and b being the Residue function curve. This yields the tissue impulse response function CBF · R(t) when iteratively solving for the elements of b. A stable solution to this is obtained either by Singular Value Decomposition (SVD) [58] or by applying noise-suppression techniques while minimizing the vector norm ||A · b − c|| often exploiting regularization [59] (forcing the solution to be well-behaved or to satisfy a-priori, user-defined conditions). The optimal approach to be adopted in this regard is still an open question [54]. However, deconvolution by SVD1 is independent upon microvascular hemodynamics and can provide precise perfusion estimates also in clinical EPI measurements [32]. If arterial delays are observed, also the peak of the deconvolved curve is delayed (i.e., it is not maximal for t=0), and CBF corresponds to the curve
peak height (when considering R(arg maxt CBF · R(t) ) ' 1)). Eventually, the calculation of MTT arises from the Central Volume Theorem (derived from the mass conservation law by Stewart [60]) CBV CBF

.

(A.8)

lP

MT T =

S = S0 exp (−bD) ,

(A.9)

Jo

ur

na

Appendix A.2. Diffusion-Weighted Imaging and IVIM maps computation Diffusion Weighted MR Imaging (DWI) has mainly focused on the investigation of alterations of diffusion patterns due to neurological disorders, acute brain ischemia and tumors [61, 18, 43]. The b-value (or b-factor) [62] summarizes gradient effects that govern the echo signal amplitude (S) so that its attenuation caused by diffusion can be expressed as

where S0 is the echo amplitude without any diffusion weighting gradient (null b-value) and D is the true diffusion coefficient. In the context of in-vivo diffusion MRI, true diffusion effects and pseudodiffusion processes related to perfusion in the microvascular level, which can In SVD the A−1 matrix is decomposed into three matrices (A−1 = V · W · U T ) so that the computation of b, thus the tissue impulse response function, is straightforward: 1

b = V · W · (U T · c) .

(A.7)

Nulling smaller diagonal elements of W allows the removal of oscillations and noise in the matrix A, resulting in a b that can be shown to be the best possible solution in a least squared sense [31].

22

Journal Pre-proof

ro

of

be disentangled following the IntraVoxel Incoherent Motion (IVIM) theory, have been jointed into the Apparent Diffusion Coefficient (ADC). Pseudodiffusion results from the motion of blood water molecules in randomly oriented capillaries that collectively can be seen as random and characterized by a pseudo-diffusion coefficient D∗ around 10−2 mm2 /s. As D∗ is close to D (D ∼ 10−3 mm2 /s for water molecules), diffusion MRI results are sensitive to both diffusion and blood microcirculation. Together with D∗ , the perfusion fraction f has been introduced into the IVIM model, representing the flowing blood fraction. Since f is usually low and D∗ > D, the perfusion driven IVIM signature has more impact on the diffusion-driven signal decay of the tissue at small b-values (b < 200-250 s/mm2 ). Thus, the IVIM model can be represented by a double-exponential model of the form

-p

S)(b) = S0 {f exp [−b(D∗ )] + (1 − f ) exp (−bD)}

.

(A.10)

ur

na

lP

re

A graphic representation of the main diffusion and perfusion parameters is presented in Figure 5. Classical (CBV, CBF and MTT) and IVIM (fIVIM and D∗ ) parameters can be linked through analytic expressions [16], if we assume that the tracer is purely intravascular and well-mixed with blood, and that the average blood velocity and the mean capillary segment length l are tissue constant, so that: CBV = f · wf

(A.11)

Ll 6D∗

(A.12)

MT T =

Jo

6wf · f D∗ (A.13) Ll where wf is the MRI-visible water content fraction and L the total capillary length (from artery to vein). CBF =

References [1] K. Schmainda, M. Prah, S. Rand, Y. Liu, B. Logan, M. Muzi, S. Rane, X. Da, Y.-F. Yen, J. Kalpathy-Cramer, et al., Multisite concordance of dsc-mri analysis for brain tumors: results of a national cancer institute quantitative imaging network collaborative project, American Journal of Neuroradiology 39 (6) (2018) 1008–1016. 23

Journal Pre-proof

[2] P. Svolos, E. Kousi, E. Kapsalaki, K. Theodorou, I. Fezoulidis, C. Kappas, I. Tsougos, The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives, Cancer Imaging 14 (1) (2014) 20. [3] B. Tamrazi, M. S. Shiroishi, C.-S. J. Liu, Advanced imaging of intracranial meningiomas, Neurosurgery Clinics 27 (2) (2016) 137–143.

ro

of

[4] J. M. Provenzale, S. Mukundan, D. P. Barboriak, Diffusion-weighted and perfusion mr imaging for brain tumor characterization and assessment of treatment response, Radiology 239 (3) (2006) 632–649.

-p

[5] H. Zhang, L. A. R¨odiger, T. Shen, J. Miao, M. Oudkerk, Perfusion mr imaging for differentiation of benign and malignant meningiomas, Neuroradiology 50 (6) (2008) 525–530.

lP

re

[6] P. Wesseling, D. J. Ruiter, P. C. Burger, Angiogenesis in brain tumors; pathobiological and clinical aspects, Journal of neuro-oncology 32 (3) (1997) 253–265.

na

[7] A. Bjørnerud, K. E. Emblem, A fully automated method for quantitative cerebral hemodynamic analysis using dsc–mri, Journal of Cerebral Blood Flow & Metabolism 30 (5) (2010) 1066–1078.

Jo

ur

[8] M. Essig, M. S. Shiroishi, T. B. Nguyen, M. Saake, J. M. Provenzale, ` Rovira, M. Wintermark, et al., D. Enterline, N. Anzalone, A. D¨orfler, A. Perfusion mri: the five most frequently asked technical questions, American Journal of Roentgenology 200 (1) (2013) 24–34. [9] Q. T. Ostrom, H. Gittleman, G. Truitt, A. Boscia, C. Kruchko, J. S. Barnholtz-Sloan, Cbtrus statistical report: Primary brain and other central nervous system tumors diagnosed in the united states in 2011–2015, Neuro-oncology 20 (suppl 4) (2018) iv1–iv86. [10] A. Holveck, S. Grand, S. Boini, M. Kirchin, J.-F. Le Bas, J.-L. Dietemann, S. Bracard, S. Kremer, Dynamic susceptibility contrast-enhanced mri evaluation of cerebral intraventricular tumors: preliminary results, Journal of Neuroradiology 37 (5) (2010) 269–275. [11] A. Villringer, B. R. Rosen, J. W. Belliveau, J. L. Ackerman, R. B. Lauffer, R. B. Buxton, Y.-S. Chao, V. J. Wedeenand, T. J. Brady, Dynamic 24

Journal Pre-proof

imaging with lanthanide chelates in normal brain: contrast due to magnetic susceptibility effects, Magnetic resonance in medicine 6 (2) (1988) 164–174.

of

[12] A. M. Smith, C. B. Grandin, T. Duprez, F. Mataigne, G. Cosnard, Whole brain quantitative cbf, cbv, and mtt measurements using mri bolus tracking: implementation and application to data acquired from hyperacute stroke patients, Journal of Magnetic Resonance Imaging 12 (3) (2000) 400–410.

-p

ro

[13] L. Orsingher, S. Piccinini, G. Crisi, Differences in dynamic susceptibility contrast mr perfusion maps generated by different methods implemented in commercial software, Journal of computer assisted tomography 38 (5) (2014) 647–654.

lP

re

[14] L. Willats, F. Calamante, The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast mri, NMR in Biomedicine 26 (8) (2013) 913–931.

na

[15] C. Federau, Intravoxel incoherent motion mri as a means to measure in vivo perfusion: A review of the evidence, NMR in biomedicine 30 (11) (2017) e3780.

ur

[16] D. L. Bihan, R. Turner, The capillary network: a link between ivim and classical perfusion, Magnetic resonance in medicine 27 (1) (1992) 171–178.

Jo

[17] D. Le Bihan, What can we see with ivim mri?, Neuroimage. [18] C. Federau, R. Meuli, K. O’Brien, P. Maeder, P. Hagmann, Perfusion measurement in brain gliomas with intravoxel incoherent motion mri, American Journal of Neuroradiology 35 (2) (2014) 256–262. [19] S. Bisdas, C. Braun, M. Skardelly, J. Schittenhelm, T. H. Teo, C. H. Thng, U. Klose, T. S. Koh, Correlative assessment of tumor microcirculation using contrast-enhanced perfusion mri and intravoxel incoherent motion diffusion-weighted mri: is there a link between them?, NMR in Biomedicine 27 (10) (2014) 1184–1191.

25

Journal Pre-proof

[20] M. Durante, R. Orecchia, J. S. Loeffler, Charged-particle therapy in cancer: clinical uses and future perspectives, Nature Reviews Clinical Oncology 14 (8) (2017) 483. [21] S. Adeberg, S. B. Harrabi, V. Verma, D. Bernhardt, N. Grau, J. Debus, S. Rieken, Treatment of meningioma and glioma with protons and carbon ions, Radiation Oncology 12 (1) (2017) 193.

ro

of

[22] J. Conklin, C. Heyn, M. Roux, M. Cerny, M. Wintermark, C. Federau, A simplified model for intravoxel incoherent motion perfusion imaging of the brain, American Journal of Neuroradiology 37 (12) (2016) 2251– 2257.

re

-p

[23] J. Boxerman, K. Schmainda, R. Weisskoff, Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not, American Journal of Neuroradiology 27 (4) (2006) 859–867.

na

lP

[24] O. Thilmann, E.-M. Larsson, I. M. Bj¨orkman-Burtscher, F. St˚ ahlberg, R. Wirestam, Comparison of contrast agents with high molarity and with weak protein binding in cerebral perfusion imaging at 3 t, Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 22 (5) (2005) 597–604.

ur

[25] P. Korfiatis, B. Erickson, The basics of diffusion and perfusion imaging in brain tumors, Applied radiology 43 (7) (2014) 22.

Jo

[26] P. Zaffino, P. Raudaschl, K. Fritscher, G. C. Sharp, M. F. Spadea, plastimatch mabs, an open source tool for automatic image segmentation, Medical physics 43 (9) (2016) 5155–5160. [27] T. J. Carroll, H. A. Rowley, V. M. Haughton, Automatic calculation of the arterial input function for cerebral perfusion imaging with mr imaging, Radiology 227 (2) (2003) 593–600. [28] F. Calamante, Arterial input function in perfusion mri: a comprehensive review, Progress in nuclear magnetic resonance spectroscopy 74 (2013) 1–32.

26

Journal Pre-proof

[29] K. Mouridsen, S. Christensen, L. Gyldensted, L. Østergaard, Automatic selection of arterial input function using cluster analysis, Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 55 (3) (2006) 524–531.

of

[30] S. Van Huffel, J. Vandewalle, M. C. De Roo, J. Willems, Reliable and efficient deconvolution technique based on total linear least squares for calculating the renal retention function, Medical and Biological Engineering and Computing 25 (1) (1987) 26–33.

-p

ro

[31] L. Østergaard, R. M. Weisskoff, D. A. Chesler, C. Gyldensted, B. R. Rosen, High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. part i: Mathematical approach and statistical analysis, Magnetic resonance in medicine 36 (5) (1996) 715– 725.

na

lP

re

[32] M. Ibaraki, H. Ito, E. Shimosegawa, H. Toyoshima, K. Ishigame, K. Takahashi, I. Kanno, S. Miura, Cerebral vascular mean transit time in healthy humans: a comparative study with pet and dynamic susceptibility contrast-enhanced mri, Journal of Cerebral Blood Flow & Metabolism 27 (2) (2007) 404–413.

ur

[33] C. Federau, P. Maeder, K. OBrien, P. Browaeys, R. Meuli, P. Hagmann, Quantitative measurement of brain perfusion with intravoxel incoherent motion mr imaging, Radiology 265 (3) (2012) 874–881.

Jo

[34] S. Chabert, J. Verdu, G. Huerta, C. Montalba, P. Cox, R. Riveros, S. Uribe, R. Salas, A. Veloz, Impact of b-value sampling scheme on brain ivim parameter estimation in healthy subjects, Magnetic Resonance in Medical Sciences (2019) mp–2019. [35] L. Yiping, S. Kawai, W. Jianbo, L. Li, G. Daoying, Y. Bo, Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: a prospective pilot study, Journal of the neurological sciences 372 (2017) 60–69. [36] J. D. Evans, Straightforward statistics for the behavioral sciences., Thomson Brooks/Cole Publishing Co, 1996.

27

Journal Pre-proof

[37] A. Romano, M. R. Espagnet, L. Calabria, V. Coppola, L. F. Talamanca, V. Cipriani, G. Minniti, A. Pierallini, L. Fantozzi, A. Bozzao, Clinical applications of dynamic susceptibility contrast perfusion-weighted mr imaging in brain tumours, La radiologia medica 117 (3) (2012) 445–460. [38] N. Binesh, M. M. Maya, H. Schultze-Haakh, F. G. Moser, Brain perfusion; how & why, H¨amtad den 15.

ro

of

[39] G.-H. Jahng, K.-L. Li, L. Ostergaard, F. Calamante, Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques, Korean journal of radiology 15 (5) (2014) 554–577.

-p

[40] B. R. Rosen, J. W. Belliveau, J. M. Vevea, T. J. Brady, Perfusion imaging with nmr contrast agents, Magnetic resonance in medicine 14 (2) (1990) 249–265.

lP

re

[41] J. Ruminski, A comparison of mtt calculation techniques in mri brain perfusion imaging, in: 4th European Conference of the International Federation for Medical and Biological Engineering, Springer, 2009, pp. 509–512.

na

[42] A. M. Paschoal, R. F. Leoni, A. C. dos Santos, F. F. Paiva, Intravoxel incoherent motion mri in neurological and cerebrovascular diseases, NeuroImage: Clinical 20 (2018) 705–714.

Jo

ur

[43] H. Kim, C. Suh, N. Kim, C.-G. Choi, S. Kim, Histogram analysis of intravoxel incoherent motion for differentiating recurrent tumor from treatment effect in patients with glioblastoma: initial clinical experience, American Journal of Neuroradiology 35 (3) (2014) 490–497. [44] W.-C. Wu, Y.-F. Chen, H.-M. Tseng, S.-C. Yang, P.-C. My, Caveat of measuring perfusion indexes using intravoxel incoherent motion magnetic resonance imaging in the human brain, European radiology 25 (8) (2015) 2485–2492. [45] C. Toh, K.-C. Wei, C. Chang, Y.-W. Peng, S.-H. Ng, H.-F. Wong, C.-P. Lin, Assessment of angiographic vascularity of meningiomas with dynamic susceptibility contrast-enhanced perfusion-weighted imaging and diffusion tensor imaging, American Journal of Neuroradiology 35 (2) (2014) 263–269. 28

Journal Pre-proof

[46] N. S. Hussain, M. D. Moisi, B. Keogh, B. J. McCullough, S. Rostad, D. Newell, R. Gwinn, G. Foltz, M. Mayberg, B. Aguedan, et al., Dynamic susceptibility contrast and dynamic contrast-enhanced mri characteristics to distinguish microcystic meningiomas from traditional grade i meningiomas and high-grade gliomas, Journal of neurosurgery 126 (4) (2017) 1220–1226.

ro

of

[47] S. Kremer, S. Grand, C. Remy, B. Pasquier, A. Benabid, S. Bracard, J. Le Bas, Contribution of dynamic contrast mr imaging to the differentiation between dural metastasis and meningioma, Neuroradiology 46 (8) (2004) 642–648.

re

-p

[48] D. van Westen, E. T. Petersen, R. Wirestam, R. Siemund, K. M. Bloch, F. St˚ ahlberg, I. M. Bj¨orkman-Burtscher, L. Knutsson, Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours, Magnetic Resonance Materials in Physics, Biology and Medicine 24 (4) (2011) 211–223.

lP

[49] J. L. Hintze, R. D. Nelson, Violin plots: a box plot-density trace synergism, The American Statistician 52 (2) (1998) 181–184.

ur

na

[50] T. J. Carroll, S. Horowitz, W. Shin, J. Mouannes, R. Sawlani, S. Ali, J. Raizer, S. Futterer, Quantification of cerebral perfusion using the bookend technique: an evaluation in cns tumors, Magnetic resonance imaging 26 (10) (2008) 1352–1359.

Jo

[51] R. Weisskoff, C. S. Zuo, J. L. Boxerman, B. R. Rosen, Microscopic susceptibility variation and transverse relaxation: theory and experiment, Magnetic Resonance in Medicine 31 (6) (1994) 601–610. [52] J. L. Boxerman, L. M. Hamberg, B. R. Rosen, R. M. Weisskoff, Mr contrast due to intravascular magnetic susceptibility perturbations, Magnetic Resonance in Medicine 34 (4) (1995) 555–566. [53] C. Z. Simonsen, L. Østergaard, P. Vestergaard-Poulsen, L. Røhl, A. Bjørnerud, C. Gyldensted, Cbf and cbv measurements by uspio bolus tracking: reproducibility and comparison with gd-based values, Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 9 (2) (1999) 342–347.

29

Journal Pre-proof

[54] L. Østergaard, Principles of cerebral perfusion imaging by bolus tracking, Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 22 (6) (2005) 710–717. [55] K. L. Zierler, Theoretical basis of indicator-dilution methods for measuring flow and volume, Circulation Research 10 (3) (1962) 393–407.

ro

of

[56] D. Bedekar, T. Jensen, K. M. Schmainda, Standardization of relative cerebral blood volume (rcbv) image maps for ease of both inter-and intrapatient comparisons, Magnetic resonance in medicine 64 (3) (2010) 907–913.

re

-p

[57] K. A. Rempp, G. Brix, F. Wenz, C. R. Becker, F. G¨ uckel, W. J. Lorenz, Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced mr imaging., Radiology 193 (3) (1994) 637–641.

na

lP

[58] H.-L. Liu, Y. Pu, Y. Liu, L. Nickerson, T. Andrews, P. T. Fox, J.-H. Gao, Cerebral blood flow measurement by dynamic contrast mri using singular value decomposition with an adaptive threshold, Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 42 (1) (1999) 167–172.

Jo

ur

[59] T. A. Bronikowski, C. A. Dawson, J. H. Linehan, Model-free deconvolution techniques for estimating vascular transport functions, International journal of bio-medical computing 14 (5) (1983) 411–429. [60] G. Stewart, Researches on the circulation time in organs and on the influences which affect it, The Journal of physiology 15 (1-2) (1893) 1–89. [61] J. M. Baehring, R. K. Fulbright, Diffusion-weighted mri in neurooncology, CNS oncology 1 (2) (2012) 155–167. [62] D. Le Bihan, E. Breton, D. Lallemand, P. Grenier, E. Cabanis, M. LavalJeantet, Mr imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders., Radiology 161 (2) (1986) 401–407.

30

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6