Purpose: To develop an accurate feature based 3D‐3D deformable registration method for patient‐specific motion model used in external beam radiation treatment of lung cancers based on a 4D computed tomography (4DCT) image set by utilizing unique features of the bifurcations of tubular organs. Method and Materials: Each 4DCT set consisted of 10 phases of 3DCT volumes during one breath cycle. A 3D tubular organ segmentation was first performed on each of the phases to extract the centerlines of bronchial trees, estimate radius of bronchial trees and automatically detect the bifurcation points by applying learning algorithm with specially designed filters. A novel deformable registration method was applied to minimize the distances of the corresponding bifurcation points between a target phase and a reference phase (e.g., between 0% phase and 50% phase) to capture the transformation between different phases. The results were evaluated by using volume and distance based estimators. Results: The learning method was trained and tested with positive and negative examples. The generalization error of the learning method was estimated using bootstrapping with the mean error rate 4.6%. The detailed quantitative and qualitative registration results are shown in the supporting materials. The mean distance estimator yielded results ranging from 1.93 mm to 4.46 mm between the corresponding points between the 0% phase images and the 50% phase images after the deformable registration. The root‐mean‐square error ranged from 1.99 mm to 5.13 mm. Conclusions: A novel and accurate 3D‐3D registration method based on the bifurcations of the tubular organs was developed to capture the transformation between the 3D CT images in the 4D computed tomography (4DCT) image sets. These preliminary results show that the proposed method is robust, fast and accurate for the deformable registration of the tubular organ in the lung.
Purpose: To develop a fast landmarks based deformable registration method to capture the soft tissue transformation between the planning 3D CT images and treatment 3D cone‐beam CT (CBCT) images for the adaptive external beam radiotherapy (EBRT). Method and Materials: The developed method was based on a global‐to‐local landmarks based deformable registration algorithm. The landmarks were first acquired by applying a fast segmentation method using active shape model. The global registration method was applied to establish a registration framework. The Laplacian surface deformation (LSD) and Laplacian surface optimization (LSO) method were then employed for local deformation and remeshing respectively to reach an optimal registration solution. In LSD the deformed mesh is generated by minimizing the quadratic energy to keep the shape and to move control points to the target position. In LSO a mesh is reconstructed by minimizing the quadratic energy to smooth the object by minimizing the difference while keeping the landmarks unchanged. The method was applied on 6 EBRT prostate datasets. The distance and volume based estimators were used to evaluate the results. The target volumes delineated by physicians were used as gold standards in the evaluation. Results: The entire segmentation and registration processing time was within 1 minute for all the datasets. The mean distance estimators ranged from 0.43 mm to 2.23 mm for the corresponding model points between the treatment CBCT images and the registered planning images. The mean overlap ratio ranged from 84.5% to 93.6% of the prostate volumes after registration. These results demonstrated reasonably good agreement between the developed method and the gold standards. Conclusions: A novel and fast landmarks based deformable registration method is developed to capture the soft tissue transformation between the planning and treatment images. The preliminary results show that the method has the potential to be applied to real‐time adaptive radiotherapy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.