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|Title:||Fast and accurate online sequential learning of respiratory motion with random convolution nodes for radiotherapy applications||Authors:||Wang, Yubo
Veluvolu, Kalyana C.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2020||Source:||Wang, Y., Yu, Z., Sivanagaraja, T. & Veluvolu, K. C. (2020). Fast and accurate online sequential learning of respiratory motion with random convolution nodes for radiotherapy applications. Applied Soft Computing Journal, 95, 106528-. https://dx.doi.org/10.1016/j.asoc.2020.106528||Journal:||Applied Soft Computing Journal||Abstract:||Accurate prediction of tumor motion for motion adaptive radiotherapy has been a challenge as respiration-induced motion is non-stationary in nature and often subjected to irregularities. Despite having a plethora of works for predicting this motion, their tracking capabilities are usually prone to large prediction errors due to the time-varying irregularities and intra-trace variabilities. To overcome this, prediction models are re-trained at regular intervals. This solution however demands a trade-off between the re-training interval and prediction accuracy in estimating the future tumor location. This is because re-training with small interval increases the computational requirements whereas a larger interval hampers the prediction performance. To address these issues, a prediction model that relies on random convolution nodes (RCN) governed by local receptive fields (LRFs) is proposed for respiratory motion prediction. The innate nature of LRFs extracts the features that contribute to the local-patterns as well as the non-stationary patterns in recent samples and subsequently learn them using extreme learning machine (ELM) theories. To address the re-training issue, we propose an online sequential learning framework (OS-fRCN) that can update the model parameters at regular intervals. Suitability of the proposed OS-fRCN for respiratory motion prediction is evaluated on 304 respiratory motion traces. Performance analysis conducted at four prediction horizons (in-line with the commercially available radiotherapy systems) demonstrated that the proposed OS-fRCN method requires less computational complexity and yields robust, accurate prediction performance when compared with existing prediction methods.||URI:||https://hdl.handle.net/10356/155268||ISSN:||1568-4946||DOI:||10.1016/j.asoc.2020.106528||Rights:||© 2020 Elsevier B.V. All rights reserved||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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