2.2 Common Methods 1 Subject recruitment
2.2.2 Image data acquisition and analysis
Patients were scanned on a 1.5 Tesla GE Signa Echospeed scanner at the National Society for Epilepsy. Standard departmental epilepsy protocol sequences were acquired for all patients unless the patient had recently undergone such a study as part of a clinical investigation or as part of a different research project. These studies ensured that each patient was given the correct MRI diagnosis and also that there had been no recent development of a second MRI evident pathology. All controls underwent as a minimum an axial inversion recovery prepared fast spoiled gradient echo (IRP-FSPGR) volumetric sequence. The radiological assessment of all the images was performed by two experienced Neuro-radiologists, Dr John Stevens and Dr Brian Kendall.
The standard epilepsy protocol consisted of:
Sagittal T1 weighted localiser. Conventional spin echo sequence (TE / TR = 14ms / 640ms), NEX = 1, slice thickness = 5mm with 2.5 mm gap, field of view (FOV) 24x24cm with a 256x256 matrix, acquisition time = 2 min 47 s.
Coronal oblique proton density (PD) and T2 weighted. Conventional spin echo, TE/TR/NEX 30&120/2000/1, 28 slices of 5mm thickness with 0 mm gap, FOV 18x24cm with a 256x192 matrix, acquisition time = 1 min 24 s.
Coronal T1 weighted 3D volume. Inversion recovery prepared fast spoiled gradient recall (IRPFSPGR), TE/TR/NEX 4.2/15.5/1, time of inversion (TI) 450, flip angle 20°, 124 slices of 1.5mm thickness, FOV 18x24cm with a 192x256 matrix, acquisition time = 6 min 56s.
Coronal oblique fast fluid attenuation inversion recovery (Fast FLAIR). Fast FLAIR sequence, TE/TR/NEX 144/11000/1, TI 2600, 28 slices of 5mm thickness with 0mm gap, FOV 18x24cm with a 192x256 matrix, and orientated perpendicular to the long axis of the hippocampus, acquisition time = 3 min 10s.
To this series was routinely added a further volumetric sequence to allow voxel placement and subsequently for segmentation using SPM99 (Statistical Parametric
Mapping; Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London).
Axial inversion recovery prepared fast spoiled gradient echo (IRP-FSPGR). Images were 124 * 1.5mm thick with field of view (FOV) = 26 cm, and a matrix of 256*128, TE/TI/TR = 4.2/450/16 ms, flip angle 20o, acquisition time = 5 min 47 s.
2.2.2.1Voxel segmentation
The methodology of SPM segmentation is well described [www.fil.ion.ucl.ac.uk,(Ashburner and Friston, 2000)]. For the purposes of this programme the MR image is assumed to consist of a number of distinct tissue types (clusters) from which every voxel has been drawn. The intensities of the voxels belonging to each of these clusters conform to a multivariate normal distribution which can be described by a mean vector, a covariance matrix, and the number of voxels belonging to the distribution. These parameters are estimated through a series of iterative computations by comparison with an a priori approximate knowledge of the spatial distributions of these clusters in the form of probability images which have been derived from MR images of a large number of subjects. These template images were segmented into binary images of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) and all normalised into the same space using a nine parameter affine transformation. The probability images are the means of these binary images so that they contain values in the range of 0 to 1 which represents the probability of a voxel being GM WM or CSF after an image has been normalised to the same space using a nine parameter transformation.
SPM99 was applied in each of the MRS experiments in this thesis. Classification of pixels as CSF, GM or WM was not forced. For example, if a pixel was reported as having a 54% probability of being GM, 10% CSF, and 36% WM, the pixel was assumed to have contained various tissues in approximately these proportions.
MRS experiments examine a localised volume of interest (VOI) from which the concentrations of relevant neuro-metabolites are measured. In order to determine the tissue composition of the prescribed VOI the IRP-FSPGR volumetric whole brain images are first segmented as above. The co-ordinates of the prescribed VOI are then given to a locally developed programme written in SAGE 7.0 (the GE MR
spectroscopy software package, based on the Interactive Data Language (IDL:
visit
www.ittvis.com/IDL) that averages the estimated content of GM, WM, and CSF from within the VOI.In the ideal situation a VOI would be sufficiently small to allow for the exclusive measurement of a single tissue type. However VOI are limited by the available signal to noise ratio (SNR) as outlined in Equation 1.7.5. Therefore at present in vivo MRS experiments are limited to a minimum VOI of approximately 5 ml (for B0 = 1.5 T, scan duration = 5 min) and consequently the prescribed volume will usually have a heterogeneous tissue composition. It is important that the effects of this heterogeneity are recognised because metabolite concentrations are different in pure grey matter compared to pure white matter voxels(Hetherington et al., 1996;McLean et al., 2000) and may vary in concentration across brain regions(Pouwels and Frahm, 1998). The major differences are between GM or WM and CSF since CSF has negligible metabolite content(Lynch et al., 1993). Comparison of MRS VOI between regions that contain different amounts of CSF can therefore lead to the introduction of partial volume error.
Because of this potential error it has become a standard practice of the NSE MRS group to correct the obtained metabolite concentrations for the presence of CSF which will not have contributed to the final metabolite signal such that:
[Metabolite]corrected= [Metabolite]measured * 1 / (1 – CSFproportion) [Eqn. 2.2.1]
Most metabolites have a higher concentration in grey matter than in white matter(Doyle et al., 1995;McLean et al., 2000). In order to correct for this further source of partial volume error the GM proportion obtained from the segmentation process may be used as a covariant in statistical comparisons that compare subjects or groups with different grey matter proportion estimates