Auto-masked 2D 3D image registration and its validation with clinical cone-beam computed tomography

Image-guided alignment procedures in radiotherapy aim at minimizing discrepancies between the planned and the real patient setup. For that purpose, we developed a 2D 3D approach which rigidly registers a computed tomography (CT) with two x-rays by maximizing the agreement in pixel intensity between the x-rays and the corresponding reconstructed radiographs from the CT. Moreover, the algorithm selects regions of interest (masks) in the x-rays based on 3D segmentations from the pre-planning stage. For validation, orthogonal x-ray pairs from different viewing directions of 80 pelvic cone-beam CT (CBCT) raw data sets were used. The 2D 3D results were compared to corresponding standard 3D 3D CBCT-to-CT alignments. Outcome over 8400 2D 3D experiments showed that parametric errors in root mean square were