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Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head‐and‐neck radiotherapy: A simulation study using patient data
Author(s) -
Kim Youngjun,
Li Ruijiang,
Na Yong Hum,
Lee Rena,
Xing Lei
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 - 10.1118/1.4898103
Subject(s) - contouring , imaging phantom , patient registration , image registration , cone beam computed tomography , computer vision , medical imaging , artificial intelligence , iterative closest point , digital radiography , radiation treatment planning , nuclear medicine , radiography , computer science , medicine , radiation therapy , computed tomography , point cloud , radiology , computer graphics (images) , image (mathematics)
Purpose: 3D optical surface imaging has been applied to patient positioning in radiation therapy (RT). The optical patient positioning system is advantageous over conventional method using cone‐beam computed tomography (CBCT) in that it is radiation free, frameless, and is capable of real‐time monitoring. While the conventional radiographic method uses volumetric registration, the optical system uses surface matching for patient alignment. The relative accuracy of these two methods has not yet been sufficiently investigated. This study aims to investigate the theoretical accuracy of the surface registration based on a simulation study using patient data. Methods: This study compares the relative accuracy of surface and volumetric registration in head‐and‐neck RT. The authors examined 26 patient data sets, each consisting of planning CT data acquired before treatment and patient setup CBCT data acquired at the time of treatment. As input data of surface registration, patient's skin surfaces were created by contouring patient skin from planning CT and treatment CBCT. Surface registration was performed using the iterative closest points algorithm by point–plane closest, which minimizes the normal distance between source points and target surfaces. Six degrees of freedom (three translations and three rotations) were used in both surface and volumetric registrations and the results were compared. The accuracy of each method was estimated by digital phantom tests. Results: Based on the results of 26 patients, the authors found that the average and maximum root‐mean‐square translation deviation between the surface and volumetric registrations were 2.7 and 5.2 mm, respectively. The residual error of the surface registration was calculated to have an average of 0.9 mm and a maximum of 1.7 mm. Conclusions: Surface registration may lead to results different from those of the conventional volumetric registration. Only limited accuracy can be achieved for patient positioning with an approach based solely on surface information.