Nonlinear multi-resolution symmetric registration in automated segmention of sub- and allocortical structures in MRI

When this methodology is applied to human cortical structure, problems arise if left-right comparisons are desired. The innate asymmetry of the human cortex requires that both left and right models of a structure be composed in order to effectively segment the desired structure in both hemispheres. Paradoxically, defining a model in both hemispheres carries a high likelihood of introducing a symmetric bias into the results. This short-falling can be resolved via development of a symmetric average model of the cortex in which both hemispheres are equally represented, one a mirror version of the other. Methods A sub-group of 151 (80m/71f age: 60-91yr.) community-dwelling, self-caring Caucasian volunteers were chosen from a previous intensive study that aimed to define early markers or predictors of cognitive performance decline. General inclusion and exclusion criteria have been previously documented [2]. T1-volumetric brain MRI scans [Siemens Magnetom 1.5T: T1 MPRAGE- 2mm axial slice (within slice=0.86x 0.86 mm)] were acquired at the baseline examination of the study. This T1 data was then converted to the MINC format, intensity corrected using histogram spline sharpening [3] and auto-cropped by thresholding the image intensity gradient curve in all three planes. Each patient data-set is initially aligned such that the natural axis of symmetry (Y-Z plane) is perpendicular to the world-space X axis. This is performed by registering each image to its own mirror image about the Y-Z plane using rigid body-registration. The resulting translations and rotations are then halved to align the data-set; the resulting data-set is termed the aligned volume. Once all the subject volumes are aligned, they are again flipped in the Y-Z plane (mirrored, so that left is on right and right is on left); these data sets are termed aligned-flipped. As an initial symmetric target is required, a rudimentary symmetric model was generated by averaging the International Consortium for Brain Mapping (ICBM) 152 model with a left-right flipped version of itself. Both the aligned and aligned-flipped images from each patient are then linearly registered to the initial model using the hierarchical fitting procedure mritotal [4]. Each of these pairs of transformations (one for each subject) are then averaged within themselves to produce the initial linear registrations. A first generation average is then built using these registration results to be used as a target volume during the next iteration. Successive iterations then follow in a similar fashion albeit using nonlinear registration (3D step size – 16mm) to match patient data to the current model. Subsequent iterations proceed using successively higher resolution non-linear fits to further refine the model. The sizes are as follows. 1 st iter: linear. 2 nd : 16mm nonlinear. 3 rd & 4 th : 16+8mm nlin. 5 th & 6 th : 16+8+4mm nlin. 7 th : 16+8+4+2mm nlin. Results Figure 1 shows the progression of one patient during the iteration towards a symmetric target, most striking is the ability of the method to compensate for large differences between patient anatomies. This is demonstrated well via the change in shape and position of the ventricles. It is also worthwhile to note the progression towards symmetry is first seen in large structures (gross shape of left and right hemispheres) followed by more local structures (calcarine sulcus). Figure 2 shows the resulting 6 th generation non-linear symmetric average as compared to the initial linear symmetric model, the standard error of the nonlinear model is also shown to provide information on the models spatial certainty. It is apparent from the standard error image that the white matter has a consistent symmetric structure across the chosen population.