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| Description
| - This paper presents a medical application of the intelligent sensing, a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-variant periodical nature of lung tumor motion. Such estimation can be achieved by using a novel multiple time-variant seasonal autoregressive integral moving average (TVSARIMA) model in which several windows of different lengths are used to calculate correlation based time-variant periods of the motion. The proposed method provides the resulting prediction as a combination of those based on different window lengths. We have compared unweighted average, multiple regression, and multilayer perceptron (MLP) for the combinations with some conventional predictions by using real data of lung tumor motion. The proposed methods with the multiple regression and MLP based combinations showed high accurate prediction and are superior to the single TVSARIMA based prediction. The best prediction performance was achieved by using the MLP based combination. The average errors were 0.7953 ± 0.0243 mm at 0.5 s ahead and 0.8581±0.0510 mm at 1.0 s ahead predictions, respectively. The results of the proposed method are clinically sufficient and superior to the conventional methods. Thus the proposed TVSARIMA with an appropriate combination method is useful for improving the prediction performance.
- This paper presents a medical application of the intelligent sensing, a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-variant periodical nature of lung tumor motion. Such estimation can be achieved by using a novel multiple time-variant seasonal autoregressive integral moving average (TVSARIMA) model in which several windows of different lengths are used to calculate correlation based time-variant periods of the motion. The proposed method provides the resulting prediction as a combination of those based on different window lengths. We have compared unweighted average, multiple regression, and multilayer perceptron (MLP) for the combinations with some conventional predictions by using real data of lung tumor motion. The proposed methods with the multiple regression and MLP based combinations showed high accurate prediction and are superior to the single TVSARIMA based prediction. The best prediction performance was achieved by using the MLP based combination. The average errors were 0.7953 ± 0.0243 mm at 0.5 s ahead and 0.8581±0.0510 mm at 1.0 s ahead predictions, respectively. The results of the proposed method are clinically sufficient and superior to the conventional methods. Thus the proposed TVSARIMA with an appropriate combination method is useful for improving the prediction performance. (en)
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| Title
| - Intelligent Sensing of Biomedical Signals - Lung Tumor Motion Prediction for Accurate Radiotherapy
- Intelligent Sensing of Biomedical Signals - Lung Tumor Motion Prediction for Accurate Radiotherapy (en)
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| skos:prefLabel
| - Intelligent Sensing of Biomedical Signals - Lung Tumor Motion Prediction for Accurate Radiotherapy
- Intelligent Sensing of Biomedical Signals - Lung Tumor Motion Prediction for Accurate Radiotherapy (en)
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| skos:notation
| - RIV/68407700:21220/11:00188443!RIV13-MSM-21220___
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| http://linked.open...avai/predkladatel
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21220/11:00188443
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Correlation; Equations; Lungs; Mathematical model; Prediction methods; Time series analysis; Tumors (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Merging Fields Of Computational Intelligence and Sensor Technology (CompSens), 2011 IEEE Workshop On
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Bukovský, Ivo
- Homma, N.
- Ichiji, K.
- Yoshizawa, M.
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open...ain/vavai/riv/wos
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| http://linked.open.../riv/zahajeniAkce
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| number of pages
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| http://bibframe.org/vocab/doi
| - 10.1109/MFCIST.2011.5949518
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
| |
| is http://linked.open...avai/riv/vysledek
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