Guobin Zhang1,2,3, Qiyuan Sun1,2, Shan Jiang4,5, Zhiyong Yang3, Xiaodong Ma3, Haisong Jiang3. 1. Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System, Tianjin, 300384, China. 2. School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China. 3. School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. 4. School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. shanjmri@tju.edu.cn. 5. Centre for Advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China. shanjmri@tju.edu.cn.
Abstract
PURPOSE: Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images. METHODS: In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases. RESULTS: Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds. CONCLUSIONS: In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.
PURPOSE: Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images. METHODS: In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases. RESULTS: Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds. CONCLUSIONS: In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.
Authors: Haisong Liu; Gang Cheng; Yan Yu; Ralph Brasacchio; Deborah Rubens; John Strang; Lydia Liao; Edward Messing Journal: Phys Med Biol Date: 2003-05-07 Impact factor: 3.609
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Authors: Ehsan Dehghan; Junghoon Lee; Pascal Fallavollita; Nathanael Kuo; Anton Deguet; Yi Le; E Clif Burdette; Danny Y Song; Jerry L Prince; Gabor Fichtinger Journal: Med Image Anal Date: 2012-06-16 Impact factor: 8.545