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The application of PET–MR image registration in the brain

Imaging Research Solutions Limited (IRSL), Cyclotron Building, Hammersmith Hospital, DuCane Road, London W12 0NN, UK

View larger version (59K): [in a new window]   Figure 1. Pre-processing—improving information content. Image (a) shows a binding potential map of [11C]raclopride in the brain of a normal subject. While the desired endpoint of the coregistration would most likely be the ability to fuse this with its corresponding MRI, the information content of the image is insufficient to give a good result. Image (b) shows a summed image of the whole dynamic scan used to calculate image (a). This now contains enough information to achieve satisfactory calculation of the transformation parameters which can then be applied to the binding potential map.

View larger version (60K): [in a new window]   Figure 2. Voxel based coregistration. If the voxel values from exactly registered pairs of identical images are plotted against each other, a straight line is produced, as shown in (a). In (b), one image has been translated in the Y direction by a single voxel and the scattergram now has a characteristic pattern. Voxel based coregistration algorithms work to minimize the spread of this distribution. The images shown here were produced using ANALYZE AVW version 3.1 [5].

View larger version (31K): [in a new window]   Figure 3. Interpolation occurs when voxels are inserted as a result of rotation or scaling of an image. Images (b) and (c) show image (a) rotated through 45 degrees, using linear and nearest neighbour interpolation, respectively. Linear interpolation inserts voxels along the junction of the orange and purple areas which have a value which is the mean of those in the outer areas, as can be seen on the colour bar. Nearest neighbour interpolation uses the values already existing in the outer areas, which has the effect of giving a pixelated, less smooth appearance.