Jan Novak1,2, Stephanie Withey1,2, Lesley MacPherson3, and Andrew Peet1,2
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 2Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom, 3Radiology, Birmingham Children's Hospital, Birmingham, United Kingdom
Synopsis
Low grade paediatric brain tumours
are increasingly being targeted by anti-vasculature therapies. It is therefore of particular interest to
assess perfusion in these pathologies. We assessed paediatric tumour perfusion using
both arterial spin labelling (ASL) and dynamic susceptibility contrast (DSC) on
a 3T TX Philips Achieva scanner. Blood flow maps were produced for both
techniques where good qualitative agreement was found illustrated by strong,
significant pixel-by-pixel correlations.
We found that tumour blood flow measured by ASL and DSC significantly
correlated (ρ = 0.714, P = 0.047) suggesting ASL can be used instead of DSC for
this measurement.Purpose
The widely accepted brain tumour
perfusion imaging technique in adults is Dynamic Susceptibility Contrast (DSC)
which tracks a contrast agent through vasculature and tissue for a number of
applications[1]. In paediatrics however, the technique
presents a number of challenges, namely requirement of a cannula and a power
injector in conjunction with obtaining an arterial input function for
reproducibility[2]. It would therefore be favourable to use a
non-contrast method to image perfusion in children such as Arterial Spin
Labelling (ASL)[3]. We assessed correlations in perfusion metrics
produced from ASL and DSC in paediatric brain tumour patients with the premise
of using ASL as an alternative method.
Methods
Eight ASL and DSC scans were both performed on five paediatric patients
with low grade brain tumours on a Philips Achieva 3T TX. Six transverse slices with a thickness of 7
mm, a matrix size of 64 × 64 pixels and
an FOV of 240 x 240 mm were acquired.
For each slice, eight post labelling delays (PLDs) were acquired,
ranging from 200-1600 ms, using a multi-phase LOOK-LOCKER sequence employing a
flip angle of 40° and using a single shot EPI readout. The pCASL labelling slab was placed 80 mm
below the imaging slices and had duration of 1400 ms.
The DSC-MRI scan was a transverse FE-EPI scan (TR / TE = 1865 / 40 ms,
field-of-view = 240 x 240 mm, matrix = 96 x 96) with flip angle of 20°. Thirty
slices with a slice thickness of 3.5 mm each were acquired to cover the whole
brain. Contrast agent was delivered in two stages to minimize T1 effects. The
temporal resolution of the DSC scan was 1.86 s which was repeated 60 times.
In-house software developed in the
Python Programming Language was used for the analysis of data. ASL data was
fitted to an equation proposed by Dai et al.[4]
where tumour blood flow (TBF) was calculated.
For the DSC, an arterial input function was generated manually, converted
to concentration time curves and subsequently used to calculate TBF, tumour
blood volume (TBV) [2]. Relative perfusion (rTBV and rTBF) were
defined as tumour/grey matter values. ROIs
were manually drawn on co-registered T2-weighted images for healthy-appearing parietal
grey matter and tumours and transferred to the perfusion maps for further
statistical analysis using the SPSS statistical package (IBM).
Results
Figure 1 shows example perfusion
maps produced both ASL and DSC of a paediatric brain tumour patient. Visual and quantitative assessment of
perfusion images of the patients suggested the tumours were hypoperfused
compared to grey matter which is consistent with what has been shown of low
grade tumours previously[5]. The relationships between the different
metrics are demonstrated by the plots in Figure 2 where mean values for ROIs
are compared and Spearman correlations with significance values included. ASL_TBF showed a strong correlation with both
DSC_TBF and DSC_TBV. A Bland Altman
analysis showed that the CBF measurements determined by both techniques agreed
well with no bias (Figure 3). On a
pixel-by pixel basis for a whole slice ASL_CBF (cerebral blood flow) correlated
well with both DSC_CBF (mean across all patients: ρ = 0.459, p = 0.003) and
DSC_CBV (mean across all patients: ρ = 0.468, p = 0.00003)(cerebral blood
volume). Tumour histograms of ASL_TBF, DSC_TBV and DSC_TBF showed qualitatively
similar shapes, an example of which is shown in Figure 4.
Discussion
Qualitatively and quantitatively on
a pixelwise basis, the perfusion maps from DSC and ASL agree well which is
important for radiological assessment as shown in the included figures. CBF is the primary measure of perfusion using
ASL and we have shown that it correlates strongly with values from the DSC in
the tumours. The significant correlation between ASL TBF and DSC_TBV is
interesting but since these are measures of different physiological parameters,
this relationship may not hold for all tumour types. The
rTBF measurements did not significantly correlate which we believe is due to
high values in the grey matter arising from vessels in the ASL. Both the Bland Altman analysis and
qualitative observation of the histograms within tumour ROIs suggest in
children that the techniques agree well. This is particularly impressive considering
the low perfusion and small range of perfusion values observed in the tumours
patients.
Conclusions
We have found that perfusion measurements in low grade paediatric brain
tumour patients produced using two different techniques correlate well. This
suggests that ASL can provide non-invasive measurements that are equivalent to
those provided by DSC, making it a valuable alternative.
Acknowledgements
This study was funded by a Birmingham
Children’s Hospital Research Foundation project grant and a grant from Free
Radio in conjunction with Help Harry Help Others Charity. We also acknowledge
funding from the CRUK and EPSRC Cancer Imaging Programme at the Children’s
Cancer and Leukaemia Group (CCLG) in association with the MRC and Department of
Health (England) (C7809/A10342). Professor Peet is funded through an NIHR
Research Professorship, 13-0053. All scans were acquired at the NIHR 3T research centre.References
1. Korfiatis P, Erickson B (2014) The basics of diffusion and
perfusion imaging in brain tumors. Appl Radiol 43 (7):22-29
2. Withey SB, Novak J, MacPherson L, Peet AC (2015) Arterial input
function and gray matter cerebral blood volume measurements in children. J Magn
Reson Imaging. doi:10.1002/jmri.25060
3. Alsop DC, Detre JA, Golay X, Gunther M, Hendrikse J,
Hernandez-Garcia L, Lu H, Macintosh BJ, Parkes LM, Smits M, van Osch MJ, Wang DJ,
Wong EC, Zaharchuk G (2014) Recommended implementation of arterial spin-labeled
perfusion MRI for clinical applications: A consensus of the ISMRM perfusion
study group and the European consortium for ASL in dementia. Magn Reson Med.
doi:10.1002/mrm.25197
4. Dai WY, Robson PM, Shankaranarayanan A, Alsop DC (2012) Reduced
resolution transit delay prescan for quantitative continuous arterial spin
labeling perfusion imaging. Magnet Reson Med 67 (5):1252-1265. doi:Doi
10.1002/Mrm.23103
5. Yeom KW, Mitchell LA, Lober
RM, Barnes PD, Vogel H, Fisher PG, Edwards MS (2014) Arterial spin-labeled
perfusion of pediatric brain tumors. AJNR Am J Neuroradiol 35 (2):395-401.
doi:10.3174/ajnr.A3670