A semi-automated method for measuring thickness and white matter integrity of the corpus callosum
Authors: S Andronikou, B S Spottiswoode, N Tomazos
Aim. Diseases affecting cerebral white matter may lead to left-right asymmetries and atrophy of interhemispheric connections, i.e. the corpus callosum (CC). Our aim was to describe and test a semi-automated system that divides the midline CC into a number of segments and determines thickness at each, then performs fibre tracking from these segments.
Methods. Six normal female volunteers (average age 25.8 ± 6.7 years) and a female patient with diagnosed multiple sclerosis (age 26 years) were scanned on a 3T MRI. We performed diffusion-weighted imaging in 12 directions, and calculated diffusion tensors and fractional anisotropy (FA) maps from this pre-processed data. Fibre tracking from a region-of-interest encompassing the entire CC was done. This fibre data, together with FA maps and the unweighted diffusion tensor imaging (DTI) image (b = 0 s/mm2), were imported into a custom tool written in MATLAB. The midline sagittal position was carefully defined by selecting multiple midline points in coronal and axial views and rotating the image volume and fibre co-ordinates accordingly.
Using the customised tool, dorsal and ventral CC contours were manually drawn on the mid-sagittal FA image, initiating automated calculation of a contour midway between these manually drawn lines. The programme was designed to then divide the midline contour into a pre-selected number of segments; from each segment border, perpendicular spokes were projected until they intersected with the dorsal and ventral contours. This technique divided the CC into a pre-set amount of segments, the number of which was limited by the spatial resolution. It was decided to set the number at 40 to ensure that each segment depicted a contiguous strip of voxels across the CC from the dorsal to the ventral contour. The system allows these segments to then be used as seeds for separate fibre tracking in each cerebral hemisphere, and various parameters are automatically plotted as a function of distance along the midline contour. The following parameters are measurable: midline CC thickness; midline FA; fibre volume for each hemisphere (represented as a left/right ratio centred on zero) and mean fibre FA for each hemisphere (also represented as a left/right ratio centred on zero).
Results. The tool proved successful in measuring and plotting CC midline thickness and FA, but was not sensitive for peripheral white matter lesions.
Conclusions. The technique successfully determined values of CC midline thickness, FA and interhemispheric differences. Future research will determine normal values for age and compare CC thickness with peripheral white matter volume loss in large groups of patients, using the semi-automated technique.