Prediction of Respiratory Motion for Radiotherapy Applications - Rasheed, Asad; Veluvolu, Kalyana C.
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Document from the year 2022 in the subject Medicine - Radiology, Nuclear Medicine, , language: English, abstract: Respiratory motion exhibits non-linear and non-stationary behavior in nature and this has been a great hindrance to the accurate prediction of tumor in motion adaptive radiotherapy. Accurate prediction of respiratory motion and subsequent tracking of tumor has been a challenge due to its irregularities and intra-trace variabilities. In order to overcome this issue, prediction models can be trained by using neural networks. In this book, we explore the usage of random vector…mehr

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Document from the year 2022 in the subject Medicine - Radiology, Nuclear Medicine, , language: English, abstract: Respiratory motion exhibits non-linear and non-stationary behavior in nature and this has been a great hindrance to the accurate prediction of tumor in motion adaptive radiotherapy. Accurate prediction of respiratory motion and subsequent tracking of tumor has been a challenge due to its irregularities and intra-trace variabilities. In order to overcome this issue, prediction models can be trained by using neural networks. In this book, we explore the usage of random vector function link (RVFL) based neural networks to train the model in a very efficient way to achieve high accuracy in respiratory motion prediction. In RVFL, the direct link from input features to output layer acts as regularization to prevent the network from overfitting. Further, the non-iterative nature of RVFL due to closed form solution makes it computationally fast. The method is validated on a bench mark respiration dataset.
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