A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications.
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for ac...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 14 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/29/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273831&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273831 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/29/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273831 109273831 109273831 10.1155/2015/489679 109273831 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. aug: au: Bukovsky, Ivo Homma, Noriyasu Ichiji, Kei Cejnek, Matous Slama, Matous Benes, Peter M. Bila, Jiri affil: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic sug: subj: Neural Networks (Computer) Respiration Movement Evaluation Lung Neoplasms Radiotherapy Radiotherapy Methods Algorithms Time Series Evaluation Research Human Funding Source ab: During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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