| Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines. | |
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MedLine Citation:
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PMID: 18660557 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Various problems with the current state-of-the-art techniques for gated radiotherapy have prevented this new treatment modality from being widely implemented in clinical routine. These problems are caused mainly by applying various external respiratory surrogates. There might be large uncertainties in deriving the tumor position from external respiratory surrogates. While tracking implanted fiducial markers has sufficient accuracy, this procedure may not be widely accepted due to the risk of pneumothorax. Previously, we have developed a technique to generate gating signals from fluoroscopic images without implanted fiducial markers using template matching methods (Berbeco et al 2005 Phys. Med. Biol. 50 4481-90, Cui et al 2007b Phys. Med. Biol. 52 741-55). In this note, our main contribution is to provide a totally different new view of the gating problem by recasting it as a classification problem. Then, we solve this classification problem by a well-studied powerful classification method called a support vector machine (SVM). Note that the goal of an automated gating tool is to decide when to turn the beam ON or OFF. We treat ON and OFF as the two classes in our classification problem. We create our labeled training data during the patient setup session by utilizing the reference gating signal, manually determined by a radiation oncologist. We then pre-process these labeled training images and build our SVM prediction model. During treatment delivery, fluoroscopic images are continuously acquired, pre-processed and sent as an input to the SVM. Finally, our SVM model will output the predicted labels as gating signals. We test the proposed technique on five sequences of fluoroscopic images from five lung cancer patients against the reference gating signal as ground truth. We compare the performance of the SVM to our previous template matching method (Cui et al 2007b Phys. Med. Biol. 52 741-55). We find that the SVM is slightly more accurate on average (1-3%) than the template matching method, when delivering the target dose. And the average duty cycle is 4-6% longer. Given the very limited patient dataset, we cannot conclude that the SVM is more accurate and efficient than the template matching method. However, our preliminary results show that the SVM is a potentially precise and efficient algorithm for generating gating signals for radiotherapy. This work demonstrates that the gating problem can be considered as a classification problem and solved accordingly. |
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Authors:
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Ying Cui; Jennifer G Dy; Brian Alexander; Steve B Jiang |
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Publication Detail:
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Type: Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. Date: 2008-07-25 |
Journal Detail:
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Title: Physics in medicine and biology Volume: 53 ISSN: 0031-9155 ISO Abbreviation: Phys Med Biol Publication Date: 2008 Aug |
Date Detail:
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Created Date: 2008-08-01 Completed Date: 2008-10-23 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0401220 Medline TA: Phys Med Biol Country: England |
Other Details:
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Languages: eng Pagination: N315-27 Citation Subset: IM |
Affiliation:
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Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Fluoroscopy / instrumentation, methods* Humans Lung Neoplasms / radiography*, radiotherapy* Pattern Recognition, Automated Radiographic Image Interpretation, Computer-Assisted / methods* Radiotherapy, Computer-Assisted / methods* |
| Grant Support | |
ID/Acronym/Agency:
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1R21 CA 110177-01A1/CA/NCI NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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