A new point-based warping method for enhanced and simplified analysis of functional brain image data.

Comparison of brain imaging data requires the exact matching of data sets from different individuals. Warping methods, used to optimize matching of data sets, can exploit either local gray value distribution or identifiable reference points within the images to be compared. Gray value-based warping, which is more comfortable, cannot be used if gray values include functional information that should be compared between images. A major drawback in the use of point-based warping methods is the lack of methods for efficient and precise definition of reference points (landmarks) within comparable data sets. Here, we present a novel approach to automatically detect sufficient numbers of landmarks, which is based on 3D differential operators. In addition, we have developed a new distance-weighted warping method, which optimizes individual local weighting factors of displacement vectors. The quality of the methods was evaluated using a set of autoradiographs documenting the metabolic activity of gerbil brains after acoustic stimulation. The new warping method was compared with known methods of landmark-based warping, i.e., warping with radial basis functions and with distance-weighted methods. For the data sets presented in this study our new optimized warping method produced an increase in linear cross correlation of 4.44%, an increase in volume overlap index of 1.55%, and a decrease in the registration error of 36.2%. In addition, the detection of functional differences was improved after warping. Therefore, the new method is a powerful tool, which enhances the comparison of complex biological structures and the quantitative evaluation of functional imaging data.