PURPOSE: The aim of this study is to develop a surface-based deformable image registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, image-guided radiation therapy, and assessment of geometrical change during and after therapy. METHODS AND MATERIALS: A surface-model-based deformable image registration system has been developed that enables quantitative description of geometrical change in multimodal images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. RESULTS: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest registration error (direction, mean +/- SD) for lung, liver and prostate were (inferior-superior, -0.21 +/- 0.38 cm), (anterior-posterior, -0.09 +/- 0.34 cm), and (left-right, 0.04 +/- 0.38 cm) respectively, which was within the image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g., 10 s for 2 prostate datasets), and deformed axial images could be viewed at interactive speed (less than 1 s for 512 x 512 voxels). CONCLUSIONS: Surface-based deformable image registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.
PURPOSE: The aim of this study is to develop a surface-based deformable image registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, image-guided radiation therapy, and assessment of geometrical change during and after therapy. METHODS AND MATERIALS: A surface-model-based deformable image registration system has been developed that enables quantitative description of geometrical change in multimodal images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. RESULTS: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest registration error (direction, mean +/- SD) for lung, liver and prostate were (inferior-superior, -0.21 +/- 0.38 cm), (anterior-posterior, -0.09 +/- 0.34 cm), and (left-right, 0.04 +/- 0.38 cm) respectively, which was within the image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g., 10 s for 2 prostate datasets), and deformed axial images could be viewed at interactive speed (less than 1 s for 512 x 512 voxels). CONCLUSIONS: Surface-based deformable image registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.
Authors: Ali Uneri; Sajendra Nithiananthan; Sebastian Schafer; Yoshito Otake; J Webster Stayman; Gerhard Kleinszig; Marc S Sussman; Jerry L Prince; Jeffrey H Siewerdsen Journal: Med Phys Date: 2013-01 Impact factor: 4.071
Authors: Edward Castillo; Richard Castillo; Josue Martinez; Maithili Shenoy; Thomas Guerrero Journal: Phys Med Biol Date: 2010-01-07 Impact factor: 3.609