An Open-Source 2D/3D-Image-Registration Algorithm: Cranial Image Guided Radiotherapy
G Warmerdam1,4, P Steininger2,3, M Neuner2,3, G Sharp4,5, B Winey4,5*, (1) Eindhoven University of Technology, Eindhoven, ,(2) Institute for Research and Development on Advanced Radiation Technologies, Salzburg, Austria ,(3) Paracelsus Medical University, Salzburg,Austria ,(4) Massachusetts General Hospital, Boston, MA, (5) Harvard Medical School, BOSTON, MATH-C-BRA-10 Thursday 10:30:00 AM - 12:30:00 PM Room: Ballroom A
Purpose: To determine the robustness and accuracy of an open source 2D/3D GPU accelerated image registration algorithm in the context of cranial image guided radiotherapy.
Methods: The open source 2D/3D image registration algorithm, Reg23, has been released under the GNU license. The algorithm utilizes an iterative digitally reconstructed radiograph (DRR) approach to the image registration problem. The DRR generator is accelerated on a GPU to rapidly iterate the optimization process. Multiple cost functions are supported and were analyzed. Robustness was determined by comparing a baseline set of orthogonal kV images of a cranial phantom with a predetermined isocenter to the planned isocenter in the CT image set and introducing more than 6000 combinations of rotations and translation to the position of the isocenter in the CT. Accuracy and time efficiency of various cost function were analyzed for the virtual patient shifts. Furthermore, a set of 43 experimental orthogonal images were acquired with a linac mounted kV imaging system of predetermined physical shifts which were compared to the results of the Reg23 algorithm.
Results: The Reg23 algorithm was found to be accurate to 0.04±0.02mm for the virtual isocenter shifts and 0.23±0.40mm for real images compared to the CBCT registration results. Time to solution could be reduced from >70 s to < 40 s without a significant change in the algorithm accuracy depending upon the cost function employed.
Conclusions: The Reg23 algorithm is robust and sensitive to sub-mm variations of virtual shifts of the isocenter position. The Normalized Cross Correlation (NCC) cost function was determined to be most accurate and fastest for cranial image registration. For real experimental data, the Gradent Difference (GD) cost function was most accurate and both GD and NCC delivered results accurate to within 0.5 mm and 0.4° when compared to CBCT/CT registrations.