Abstract：
:Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods.
journal_name
Med Eng Physjournal_title
Medical engineering & physicsauthors
Tatinati S,Nazarpour K,Tech Ang W,Veluvolu KCdoi
10.1016/j.medengphy.2016.04.021subject
Has Abstractpub_date
2016-08-01 00:00:00pages
749-57issue
8eissn
1350-4533issn
1873-4030pii
S1350-4533(16)30077-7journal_volume
38pub_type
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