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Title: Real-time automatic fiducial marker tracking in low contrast cine-MV images

Abstract

Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform moremore » sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images. Conclusions: The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.« less

Authors:
; ; ; ; ;  [1]
  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan, 62102 (China)
Publication Date:
OSTI Identifier:
22099173
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 40; Journal Issue: 1; Other Information: (c) 2013 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 60 APPLIED LIFE SCIENCES; ACCURACY; ALGORITHMS; CALCULATION METHODS; COMPUTERIZED TOMOGRAPHY; IMAGE PROCESSING; IMAGES; PATIENTS; PERFORMANCE; PROSTATE; RADIATION DOSES; RADIOTHERAPY

Citation Formats

Lin, Wei-Yang, Lin, Shu-Fang, Yang, Sheng-Chang, Liou, Shu-Cheng, Nath, Ravinder, Wu, Liu, and Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220. Real-time automatic fiducial marker tracking in low contrast cine-MV images. United States: N. p., 2013. Web. doi:10.1118/1.4771931.
Lin, Wei-Yang, Lin, Shu-Fang, Yang, Sheng-Chang, Liou, Shu-Cheng, Nath, Ravinder, Wu, Liu, & Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220. Real-time automatic fiducial marker tracking in low contrast cine-MV images. United States. doi:10.1118/1.4771931.
Lin, Wei-Yang, Lin, Shu-Fang, Yang, Sheng-Chang, Liou, Shu-Cheng, Nath, Ravinder, Wu, Liu, and Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220. Tue . "Real-time automatic fiducial marker tracking in low contrast cine-MV images". United States. doi:10.1118/1.4771931.
@article{osti_22099173,
title = {Real-time automatic fiducial marker tracking in low contrast cine-MV images},
author = {Lin, Wei-Yang and Lin, Shu-Fang and Yang, Sheng-Chang and Liou, Shu-Cheng and Nath, Ravinder and Wu, Liu and Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220},
abstractNote = {Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images. Conclusions: The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.},
doi = {10.1118/1.4771931},
journal = {Medical Physics},
issn = {0094-2405},
number = 1,
volume = 40,
place = {United States},
year = {2013},
month = {1}
}