An Extension of Iterative Closest Point Algorithm for 3D-2D Registration for Pre-treatment Validation in Radiotherapy

The paper presents a novel feature-based 3D-2D registration method to align a pair of orthogonal X-ray images to the corresponding CT volumetric data with full 6 degrees of freedom by combining the Iterative Closest Point (ICP) and Z-buffer algorithms. The proposed method has been evaluated using simulated data as well as skull phantom data. For the latter, the alignment errors were found to vary from 0.04 mm to 3.3 mm with an average of 1.27 mm for translation, and from 0.02 to 1.64 with an average of 0.82 for rotation. With the accuracy comparing favourably against other feature-based registration methods and the computational load being much less than intensity-based registration methods, the proposed method provides a good basis for validation of patient and machine set-up in the pretreatment procedure in radiotherapy.