Abstract: The paper presents a computationally efficient 3D–2D image registration algorithm for automatic pre-treatment validation in radiotherapy. The novel aspects of the algorithm include (a) a hybrid cost function based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomical contours and a level set term for similarity measurement; and (b) a fast search method based on parabola fitting and sensitivity-based search order. Using CT and orthogonal x-ray images from a skull and a pelvis phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error). For the simulated data, high registration accuracy with average errors of 0.53 mm ± 0.12 mm for translation and 0.61° ± 0.29° for rotation within the capture range has been achieved. For the tested phantom data, the algorithm has also shown to be robust without being affected by artificial markers in the image.

Abstract: The paper presents a new intensity-based 3D-2D image registration algorithm for automatic pretreatment validation in radiotherapy. The novel aspects of the algorithm includes a hybrid cost function developed based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomic contours and level set for similarity measurement, and a fast search method developed based on parabola fitting and sensitivity based search order. Using CT and orthogonal X-ray images from a skull phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error) and high registration accuracy with average errors of 0.53± 0.12 mm for translation and 0.61°±0.29° for rotation within the capture range.

Abstract: 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.