Evaluation of IVIM Perfusion Parameters as Biomarkers for Paediatric Brain Tumours

Emma Meeus^{1,2,3}, Jan Novak^{2,3}, Stephanie Withey^{2,3,4}, Lesley MacPherson^{3}, and Andrew Peet^{2,3}

The IVIM and DSC data analysis was computed using in-house Python software (Anaconda, Continuum Analytics, v.2.7.10) and the statistical analysis in SPSS Statistics (IBM, v.22). For IVIM model simulations and patient data, non-linear least-squares minimisation was performed using the Levenberg-Marquardt algorithm. The bi-exponential fitting methods varied from a non-constrained simultaneous fitting to a more constrained step-wise fitting (Fig.1). The IVIM grey matter (GM) and tumour model data were created using IVIM parameters derived from the relevant ROIs and with the same b-value distribution as used for the patient imaging. Random Gaussian noise was added to the model data to mimic SNR levels of 10-70.

Patient data
(6 brain tumour patients aged 1-10) was acquired on a Philips Achieva 3T TX MRI
scanner at the Birmingham Children’s Hospital using 32-multichannel receiver
head coil. The DW-MRI sequence used a sensitivity-encoded (SENSE) approach with
the following parameters: 11 exponentially spaced b-values (0-1000 s mm^{-2})
in three orthogonal directions, TR/TE 4,000/91 ms, FOV 240x240, acquisition
matrix 96x96, 30 slices with thickness of 3.5 mm and in-plane resolution
2.5x2.5 mm^{2}. The scan duration was 2.12 minutes. The DSC-MRI used a protocol described
previously, as well as the computation of cerebral blood flow (CBF), cerebral
blood volume (CBV) and mean transit time (MTT). ^{4} ROIs were drawn
manually on co-registered T2-weighted images for healthy-appearing GM and
tumours and transferred to the perfusion maps for correlation analysis (Spearman
correlation coefficient, ρ).

The data simulation
results are shown in Fig.2 and Table 1. The accuracy was measured using the theoretical
IVIM value and the mean value from 1000 data iterations and the precision in terms
of coefficient of variation. In the realistic SNR_{b1000} of 30 ^{3}, the constrained 1-parameter fitting was found
to produce the overall most accurate and precise IVIM parameters f and D* with average error/CV% 11.4/77.6% and
37.6/69.9 % in the low-perfusion regime and 16.4/35.8% and 3.33%/39.7% in the
high-perfusion regime, respectively.

Significant voxel-wise correlations were observed in the GM between the IVIM-f and DSC-CBV (ρ = 0.271-0.588, P <0.01), IVIM-fD* and DSC-CBF (ρ = 0.302-0.543, P <0.01), respectively. The correlation between the IVIM-D* and DSC-MTT was not significant (ρ = 0.024-0.162, P >0.05). The voxel-wise correlations in the tumours were found more variable with only a few cases showing any significant correlation (f-CBV ρ = 0.024-0.154, D*-MTT ρ = 0.009-0.388, fD*-CBF ρ = 0.031-0.434). Relative mean (mean tumour/mean GM) correlations were also computed for the perfusion parameters (Fig.4). Significant correlation was observed between the IVIM-f and DSC-CBV parameters, using both 1-and 2-parameter fitting. Strong correlations were also observed between the IVIM-fD* and DSC-CBF.

The simulations suggest that the IVIM-f is robust enough to provide reliable results for low-perfused tumours and GM. The IVIM-D* was found to have limited robustness for the tumour model, with noticeable higher variance compared to IVIM-f (Fig.2). Overall, the 1-parameter fitting was found to be the most robust in terms of accuracy and precision, with the smallest number of outliers (Table 1).

The strong correlation of IVIM-f and DSC-CBV confirms the perfusion related origins of the f-parameter and further confirms the robustness of the 1-parameter fitting (Fig.4 and 5). The correlations between IVIM-D*, fD* and DSC-CBF, MTT, respectively, were likely reduced due to the limited robustness of IVIM-D* fitting as observed in the simulations. The IVIM-D* and DSC-MTT were found to correlate for the younger cohort (ρ = 0.800, P <0.05), but an outlier case (age 10) was found to reduce the correlation significantly. A larger cohort with a range of ages is required to further investigate the effect of age on perfusion.

1. Le Bihan D. Intravoxel Incoherent Motion Perfusion MR Imaging: A Wake-Up Call. Radiology. 2008;249(3): 748-52.

2. Cohen AD, Schieke MC, Hohenwalter MD, et al. The Effect of Low b-Values on the Intravoxel Incoherent Motion Derived Pseudodiffusion Parameter in Liver. Magn Reson Med. 2015;73(1):306-11.

3. Wu WC, Chen YF, Tseng HM, et al. Caveat of measuring perfusion indexes using Intravoxel incoherent motion magnetic resonance imaging in the human brain. Eur Radiol. 2015;25(8):2485-92.

4. Withey, SB, Novak J, Peet, A, et al. Arterial input function and gray matter cerebral blood volume measurements in children. J Magn Reson Imaging. 2015;00(0):000-000.

Fig. 1 Flow chart describing the different bi-exponential IVIM
fitting methods used in this study. The equation parameters S(b)/S(0), D, D* and f correspond to normalised signal
ratio, tissue diffusion coefficient, pseudodiffusion coefficient and perfusion fraction, respectively.

Fig. 2 IVIM
simulation results with tumour model (a-b) and GM model (c-d) with theoretical
values shown as dashed line. The IVIM D, D* and f for tumour
model were 1.63x10^{-3} mm s^{-1}, 7.23x10^{-3} mm s^{-1} and 0.0953
and for GM 8.32x10^{-4} mm s^{-1}, 2.68x10^{-2} mm s^{-1} and 0.115,
respectively.

Table 1. The
coefficient of variation (CV%) results for the data simulations using GM and
tumour model and the corresponding number of outliers using a 30 %
least-squares fitting threshold. The results are shown for all three IVIM
fitting methods.

Fig. 4 Correlation plots comparing mean relative (i.e.
tumour/grey matter) ROI values of (a) DSC-CBV with IVIM-f and (b) DSC-CBF with
relative IVIM-fD*. Spearman’s correlation coefficient (ρ) are reported for 1-parameter (ρ1) and 2-parameter (ρ2) IVIM
fitting.

Fig. 5 Metastatic optic
pathway glioma seen on axial T2-weighted image (a) and the parameter maps of IVIM-f
(b), IVIM-fD* (c), DSC-CBV (d) and DSC-CBF (e) showing the low-perfused tumour
region.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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