Rigid registration of sequentially acquired 3D images provides valuable information to the physician for therapy control. According to digital subtraction angiography we use subtracted volumes to visualize changes. For instance longitudinal dependent tumor growth or regress needs to be quantified in many medical diagnosis procedures. No commercial solution for hospitals supports the visualization of time dependent changes in mono modal imaging data yet. Current clinical practice requires a manual alignment of subjectively corresponding pictures of the sequentially acquired 3D images. The clinical workflow can be significantly improved by an automated 3D/3D rigid registration approach that is based on voxel similarity measures and uses algorithms of digital subtraction angiography that allow the visualization of longitudinal tissue changes. The work introduces a novel unbiased approach by application of transfer functions that allows the improved rigid registration of medical images with major non-rigid deformations at the region of interest.

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