An intensity-based registration algorithm for probabilistic images and its application for 2-D to 3-D image registration

Registration of 2-D projection images and 3-D volume images is still a largely unsolved problem. In order to register a pre-operative CT image to an intra-operative 2-D x-ray image, one typically computes simulated x-ray images from the attenuation coefficients in the CT image (Digital Reconstructed Radiograph, DRR). The simulated images are then compared to the actual image using intensity-based similarity measures to quantify the correctness of the current relative pose. However, the spatial information present in the CT is lost in the process of computing projections. This paper first introduces a probabilistic extension to the computation of DRRs that preserves much of the spatial separability of tissues along the simulated rays. In order to handle the resulting non-scalar data in intensity-based registration, we propose a way of computing entropy-based similarity measures such as mutual information (MI) from probabilistic images. We give an initial evaluation of the feasibility of our novel image similarity measure for 2-D to 3-D registration by registering a probabilistic DRR to a deterministic DRR computed from patient data used in frameless stereotactic radiosurgery.