Several 3D/2D registration algorithms for image-guided therapy have been introduced in the past years. Recently, we
have proposed a method which first reconstructs a 3D image from a few intraoperative 2D X-ray images and then
establishes the rigid transformation between the preoperative 3D CT or MR image and the 3D reconstructed image. The
similarity measure applied in this registration method should be able to cope, among others, with the low quality of the
reconstructed image. Using the recently proposed similarity measure evaluation protocol, we have evaluated the behavior
of five similarity measures. The measures have been evaluated with respect to: a) preoperative imaging modalities (CT
and MR); b) number of 2D images used for reconstruction; and c) number of reconstruction iterations. Increasing the
number of 2D projections or reconstruction iterations improves the accuracy but slightly worsens the robustness. We
have shown that almost all similarity measures have better properties if the optimal parameters are chosen. The most
appropriate similarity measure for this type of registration is the asymmetric multi-feature mutual information.
The spatial resolution of echo planar image (EPI) data acquired for functional MRI (fMRI) studies is low. To facilitate their interpretation, conventional T1-weighted anatomical images are often acquired prior to the acquisition of the EP images. T1-weighted and EP images are then registered and activation patterns computed from the EP images are superimposed on the anatomic images. Registration between the anatomic and the EP images is required to compensate for patient motion between the acquisitions and for geometric distortions affecting EP images. Recently, methods have been proposed to register EP and anatomic images using non-rigid registration techniques. In these approaches, the transformation is parameterized using splines. Here, we propose an alternative solution to this problem based on optical flow with non-stationary stiffness constraint. The approach we propose also includes several preprocessing steps such as automatic skull removal and intensity remapping. Results obtained with eight studies on normal volunteers are presented.