Intensity Based Rigid 2D-3D Registration Algorithms for Radiation Therapy

One of the main applications for computers in medicine is to digitally merge patient data that originates from different sources. For our 2D-3D Registration problem, this means finding the right spatial alignment of a three-dimensional Computed Tomography (CT) data set and one or two X-Ray images from the same patient. An automatic algorithm has to create a digitally reconstructed radiograph (DRR) for a specific orientation in the CT data set, compare the result with the X-Ray image, alter the position and orientation of the DRR and repeat the process until the optimal alignment is reached. A key issue is to assess the alignment of these two images in every step. I present a comprehensive examination of all intensity-based similarity measures known in literature, and use them with a number of different optimization algorithms. We developed a prototypic application for both manual and automatic registration. The performance is evaluated for different data sets, and validation is done using marker-based ground truth information. The results show that we are able to do very precise and fully automatic registration within a couple of minutes. We propose to use this technique to find the correct patient position on the treatment couch for radiation therapy. This would replace the common, often very inconvenient immobilization methods.