Correlative techniques for cross-modality medical image registration

Correlation provides rapid, accurate, objective image registration, but is limited to applications where the data sets are similar. Our goal was to develop preliminary image processing techniques which will allow correlative registration of (dissimilar) brain images obtained using different imaging modalities. A simulation showed that correlative registration is effective for trend-similar images, leading to the hypothesis that preprocessing algorithms need only impose trend similarity to permit successful cross-modality correlative registration. This hypothesis was tested using MR and CT image which were acquired with external fiducial markers to test the registration accuracy. After analyzing the salient features of each image set, trend similarity was imposed using simple masking and gray scale adjustment techniques. A correlative registration program was then applied to the trend-similar image sets, and the resulting rigid body transformation parameters were applied to the original images. The registered image sets matched the fiducial marker locations to within one pixel, accuracy is similar to that obtained by matching user-identified anatomical points, leading to the conclusion that simple pre-processing techniques can allow correlative registration to be used for aligning images from multiple imaging modalities.