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Voxel-Based Investigation Of White Matter Changes In Normal Ageing Using Diffusion Tensor Imaging And TractographyThomas R. Barrick 1 , Rebecca A. Charlton 1 , Michael O’Sullivan 1 , Hugh S. Markus 1 , Chris A. Clark 11 Centre for Clinical Neuroscience, St. George’s, University of London, United Kingdom.IntroductionIn recent years the importance of changes in white matter integrity in normal ageing has been reported (1). Diffusion tensor imaging has enabled us to investigatewhite matter structural integrity in vivo, and region of interest approaches have lead to debate about the regional specificity of white matter decline in ageing, inparticular that the frontal lobes are preferentially affected (2, 3, 4). Voxel-based statistical analyses allow us to investigate anatomical distribution of white matterdamage across the ageing brain. Here we present data from a large sample of normal older adults using voxel-based statistical analysis. We analyse 3D meandiffusivity (MD) and fractional anisotropy (FA) maps, and investigate MD and FA perpendicular to the mid-sagittal plane (using 2D column maps, 5) and throughcoronal slices (1D coronal slice profiles, 5) in standard space. Post-hoc streamline tractography was performed through significant clusters, in order to investigatethe extent of white matter pathways affected in the ageing process.MethodsMRI data acquisition and preliminary analysis: 99 healthy adults between 50 and90 years were scanned on a 1.5T GE Signa MRI system (max. field gradientstrength 22 mTm -1 ). Diffusion tensor imaging (DTI) was achieved using a singleshot echo planar sequence with 12 diffusion sensitised directions as describedpreviously (6). Two interleaved acquisitions comprising 25 slices each providedwhole brain coverage (resolution: in plane 2.5mm; through plane 2.8mm). Eachsubject’s DTI was normalised to standard space by affine transformation (7) andresampled to contain 1.0 mm 3 voxels. MD and FA images were generated foreach subject DTI and voxels corresponding to CSF were removed prior to furtheranalysis.Generation of MD and FA (2D) column and (1D) coronal slice profile maps: Foreach subject mean MD and FA values were computed perpendicular to the midsagittalplane through white matter of both cerebral hemispheres in standardspace. This generated pixelated 2D column maps (5) of MD and FA through theentire brain of each subject. In addition, coronal slice profile maps (5) weregenerated by computing the mean values of MD and FA through white matter ofboth cerebral hemispheres for each coronal slice in standard space.Statistical analysis: 3D and 2D data were smoothed (3D 3mm FWHM; 2D 2mmFWHM) and statistical analysis was performed in SPM2 using a linear correlationmodel. MD and FA 3D, 2D column and 1D coronal slice profile maps werecorrelated with age. In all analyses, multiple comparisons correction was appliedusing a family wise error at p < 0.05 corrected. Significant positive and negativecorrelations are represented using hot and cold colours, respectively, in thesignificance maps.Post-hoc tractography: Subvoxel streamline tractography was performed asdescribed previously (6). Streamlines (vector step length 1.0mm, terminationcriteria FA < 0.08) were initiated from the centre of every voxel in each Figure 1individual normalised DTI dataset. Only streamlines passing through significant3D clusters determined from the 3D MD and FA statistical analyses were retained and wereconverted to binary images. These binary images were statistically analysed on a voxelwise basisusing the binomial test including a multiple comparisons correction (p < 0.05).(i)(ii)(iii)(i)t-statistic(a) Significant MD increase-120 -100 -80 -60 -40 -20 0 20 40 60 80z=0mm151050-5-10-15Talairach y-coordinate (mm)t-statistict-sttic1-1(a) Mean Diffusivity-120 -100 -80 -60 -40 -20 0 20 40 60 80t-statistic12.92-12.92(b) Significant FA decreasez=0mmO7151050-5-10-15Talairach y-coordinate (mm)(b) Fractional AnisotropyRight Left Right LeftResultsWhite matter coronal slice profile significance maps showed MD age to exhibit a significantpositive correlation with age, such that MD increased with age across the entire brain (Figure 1ai).White matter MD column maps were also significantly correlated with age across the brain (Figure1aii). Voxel-based 3D statistical analysis of the MD maps again revealed extensive, significantvoxel clusters representing positive correlations with age across the white matter and in thethalamus (Figure 1aiii, also see Figure 2ai for surface rendering of significant voxel clusters). Theselocal changes were shown to affect white matter pathways across the entire brain in the post-hoctractography statistical analysis (Figure 2aii).Coronal slice profile significance maps showed FA to have a significant negative correlation withage, such that FA decreased with age across the entire brain (Figure 1bi). White matter FA columnmaps also showed a significant negative correlation with age across the brain (Figure 1bii) withgreater significance found in the anterior frontal lobe. Voxel-based statistical analysis of the FAmaps revealed extensive negatively correlated peri-callosal clusters located predominately in thefrontal lobes of both hemispheres (Figure 1biii, and Figure 2bi). Although significant FA clusterswere small in comparison to MD clusters post-hoc tractography revealed large networks of frontofronto,fronto-parietal and fronto-occipital pathways were affected by age-related FA changes(Figure 2b).(i)z=0mm(ii)Figure 2z-statistic9.950.0Discussion These results suggest decline of white matter integrity across the whole brain, but with the anterior frontal lobes being particularly affected. This is inkeeping with results from other authors (8). In addition, tractography results suggest that localised peri-callosal white matter degeneration may affect pathwayconnectivity throughout the whole brain. Consequently, white matter pathway degeneration may explain the pattern of impaired and spared cognitive functions thatare apparent in normal ageing. In particular, abilities that activate diffuse networks could be more affected, compared to activities relying upon localised networkswhich remain relatively stable (9). It remains to be investigated as to whether voxel-based analyses of cognitive function will support this inference.References[1] Morrison, JH & Hof PR, 1997. Science, 278: 412-419. [2] Pfefferbaum A et al., 2000. Mag Res Med, 44: 259-268.[3] Sullivan EV et al., 2001. NeuroReport, 12: 99-104. [4] Charlton RA et al., 2006. Neurology, 66(2): 217-222.[5] Barrick TR et al., 2005. NeuroImage, 24(3): 678-691. [6] Barrick TR & Clark CA, 2004. NeuroImage, 22(2): 481-491.[7] Alexander DC et al., 2001. IEEE Trans Med Imaging, 20(11): 1131-1139. [8] Head D et al., 2004. Cerebral Cortex, 14: 410-423.[9] Mega MS & Cummings JL, 1994. J of Neuropsychiatry & Clin Neuro, 6: 358-370.
O8Incorporating Domain Knowledge into Fuzzy Connectedness Image Segmentation:Application to Brain Lesion Volume Estimation in Multiple SclerosisMark A. Horsfield 1 , R. Bakshi 2 Marco Rovaris 3 , Mara A. Rocca 3 , Venkata S.R. Dandamudi 2 , Paola Valsasina 3 , Elda Judica 3 ,Fulvio Lucchini 3 , Charles Guttmann 2 , Maria Pia Sormani 4 and Massimo Filippi 31 University of Leicester, Leicester LE1 5WW; 2 Harvard Medical School, Boston MA;3 University of Milan, Italy; 4 University of Genova, ItalyIntroductionMultiple sclerosis (MS) is a neurological disorder affecting the brainand spinal cord thought to be of autoimmune origin [1]. Focal areas oftissue damage (lesions) occur, and are visible as hyperintensities onT 2 -weighted MR images. Measuring the change in volume of theselesions (the ‘lesion load’) plays an import part in placebo-controlledclinical trials of new treatments for MS.However, quantitative assessment of lesion load is time-consuming,and the volumes obtained are operator-dependent and prone tooperator-induced errors [2]. MS pathology, whilst having focalabnormalities, also has a diffuse component, and the lesions seen onMRI have no clearly-defined borders, making the delineation of suchborders highly subjective. Several workers have addressed theproblem of improving the reproducibility of the measurement of MSlesion volumes using computer-assisted or fully automatedcomputerized methods.Fuzzy connectedness is a general image segmentation framework inwhich the object membership of pixels depends on the way they “hangtogether” spatially in spite of gradual variations in their intensity.Fuzzy connectedness has previously been applied to MS lesionsegmentation, as part of a complex image analysis procedure [3]. Inthis work, we wished to simplify the task of lesion segmentation, toreduce the operator workload as much as possible. To this end weshow how prior knowledge can be incorporated into the fuzzyconnectedness framework, to improve the segmentation for thisparticular task.MethodsFuzzy Connectedness: The fuzzy affinity between any two imageelements (pixels) depends on the degree of adjacency of the pixels, aswell as the similarity of their intensity values. The closer the pixelsare, and the more similar their (possibly multi-parametric) intensities,the greater should be the affinity between them. The strength ofconnectedness between any pair of pixels (c, d) is defined byconsidering all possible connecting paths of pixels between c and d,where such a path is a sequence of links between adjacent pixels alongthe path. The strength of any one such path is the strength of theweakest link in it. Finally, the strength of connectedness between cand d is the strength of the strongest of all possible paths between cand d. Based on a set of seed pixels, provided by the user, a fuzzyconnected objected is defined as the set of all pixels with a connectionstrength to the seeds greater than a pre-set threshold.Incorporating Domain Knowledge: We modify the affinity betweenpixels to include three extra components:• The first relates to the known intensity characteristics of the imagefeature – in this case MS lesions – relative to the surrounding nonfeaturepixels. The user can provide “intensity hints” to indicate thatthe feature is brighter or darker than the background in a particularimage. This skews the implicit distribution of intensities from whichthe affinity is calculated.• The second consists of a probabilistic model of the spatial variationin the size characteristics of the features. Feature size is modelled asa spatially-varying feature membership correlation.• Third, we incorporate a probabilistic model of the known spatialdistribution of the feature to provide a “prior affinity”.Total affinity is a weighted sum of the prior affinity and the imagederivedaffinity.The feature size and distribution models were derived from asample of 300 MRI scans from the MS patient population, with thelesions manually segmented by neurologists. To assess an individualpatient’s scan, a proton-density template is registered to it, and thesame transform applied to the probability & feature size images.Testing: The method was tested against the performance of anestablished semi-automated methods of MS lesion segmentation basedon edge detection and contour following [3]. Using both methods, twooperators independently segmented the lesions in 14 MS patients fromdual-echo brain MRI scans. Each patient was scanned twice on thesame day so that we could assess both the scan-rescan and interobservervariability.The different sources of variation for the lesion volumemeasurements were modeled with a random effect analysis of variance(ANOVA), with three random factors (subject, observer and scannumber), plus three interaction terms, and a residual.In addition, the concordance between measurements was evaluatedas:A ∩ Bconcordanc e = × 100%A ∪ Bwhere A is the set of pixels segmented on one occasion and B the setsegmented on a second occasion. This is a measure of the consistencyof which pixels are delineated as lesion.ResultsFigure 1 shows one slice from the templates derived from the 300manually-segmented MRI scans.Figure 1: Representative slice from the template images: protondensityweighted template (left) lesion probability (centre); and lesionfeature size (right).The operator time required to segment the MS lesion was reducedfrom an average of 111 minutes per patient for the contouring method,to 16 minutes per patient for the fuzzy connections method.As expected, the ANOVA showed that the scan subject was by farthe biggest contributor to the total variance. However, the scannumber (first or second scan) made a neglibible contribution tovariance. A reduced model was therefore used, having removed theeffect of the scan number. Table 1 shows the non-negligiblecontributions to the variance for the reduced model.Contributor Contouring Fuzzy ConnectionsObserver 18.9% 9.3%Subject×Observer 4.4% 2.2%Table 1: contributors to variance for the two volume measurementmethods (ANOVA).Table 2 shows the mean concordances achieved when the sameobserver measured the first and second scans (scan-rescan), when thesame observer measured the same scans twice (intra-observer) andwhen the two observers measured the same scan (inter-observer).Contouring Fuzzy ConnectionsScan-rescan 55.1% 60.6%Intra-observer 74.36% 80.8%Inter-observer 47.6% 59.9%Table 2: concordances for the two volume measurement methods.ConclusionMS lesion segmentation based on the Fuzzy Connections algorithmsignificantly reduces the operator time, since the task is reduced to oneof identifying lesions and marking them, rather than delineating theborders. The method also improves the reproducibility andconsistency of the segmented lesion volumes, compared to a widelyusedsemi-automated method. The incorporation of prior knowledgeabout the distribution, size and brightness characteristics into thealgorithm is essential to achieve the required reproducibility.References1. H Lassman. In: McAlpine's Multiple Sclerosis, ChurchillLivingstone, London (1998).2. M. Filippi et al. Brain 118, 1593-1600 (1995).3. J.K. Udupa, L. Wei, S. IEEE Transactions on Medical Imaging. 16,598-609 (1997).
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