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WE‐D‐9A‐01: A Novel Mesh‐Based Deformable Surface‐Contour Registration
Author(s) -
Zhong Z,
Cai Y,
Guo X,
Jia X,
Chiu T,
Kearney V,
Liu H,
Jiang L,
Chen S,
Yordy J,
Nedzi L,
Mao W
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889417
Subject(s) - computer vision , artificial intelligence , imaging phantom , image registration , projection (relational algebra) , polygon mesh , surface (topology) , voxel , displacement (psychology) , pixel , rigid body , body surface , computer science , cone beam computed tomography , ellipsoid , iterative reconstruction , canny edge detector , geometry , mathematics , edge detection , image processing , physics , computed tomography , algorithm , image (mathematics) , optics , medicine , psychology , classical mechanics , astronomy , radiology , psychotherapist
Purpose: Initial guess is vital for 3D‐2D deformable image registration (DIR) while dealing with large deformations for adaptive radiation therapy. A fast procedure has been developed to deform body surface to match 2D body contour on projections. This surface‐contour DIR will provide an initial deformation for further complete 3D DIR or image reconstruction. Methods: Both planning CT images and come‐beam CT (CBCT) projections are preprocessed to create 0–1 binary mask. Then the body surface and CBCT projection body contours are extracted by Canny edge detector. A finite element modeling system was developed to automatically generate adaptive meshes based on the image surface. After that, the projections of the CT surface voxels are computed and compared with corresponding 2D projection contours from CBCT scans. As a result, the displacement vector field (DVF) on mesh vertices around the surface was optimized iteratively until the shortest Euclidean distance between the pixels on the projections of the deformed CT surface and the corresponding CBCT projection contour is minimized. With the help of the tetrahedral meshes, we can smoothly diffuse the deformation from the surface into the interior of the volume. Finally, the deformed CT images are obtained by the optimal DVF applied on the original planning CT images. Results: The accuracy of the surface‐contour registration is evaluated by 3D normalized cross correlation increased from 0.9176 to 0.9957 (sphere‐ellipsoid phantom) and from 0.7627 to 0.7919 (H&N cancer patient data). Under the GPU‐based implementation, our surface‐contour‐guided method on H&N cancer patient data takes 8 seconds/iteration, about 7.5 times faster than direct 3D method (60 seconds/iteration), and it needs fewer optimization iterations (30 iterations vs 50 iterations). Conclusion: The proposed surface‐contour DIR method can substantially improve both the accuracy and the speed of reconstructing volumetric images, which is helpful for applying in adaptive radiotherapy. This research is supported by CPRIT individual investigator award RP110329.