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A robust automated surface‐matching registration method for neuronavigation
Author(s) -
Fan Yifeng,
Yao Xufeng,
Xu Xiufang
Publication year - 2020
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 - 10.1002/mp.14145
Subject(s) - imaging phantom , image registration , iterative closest point , artificial intelligence , computer vision , neuronavigation , point set registration , computer science , matching (statistics) , point (geometry) , medical imaging , point cloud , image (mathematics) , mathematics , nuclear medicine , medicine , magnetic resonance imaging , radiology , statistics , geometry
Purpose The surface‐matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient‐to‐image registration. Methods A coarse‐to‐fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three‐dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4‐points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine‐registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. Results Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient’s head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. Conclusions The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.