2015
DOI: 10.1155/2015/489679
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A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

Abstract: 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… Show more

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Cited by 27 publications
(22 citation statements)
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“…In this work, an average of 28 input‐output data pairs was used for each training batch (i.e., sliding window length). This is smaller than the number of data pairs required in the other studies that use the same number of training epochs (i.e., 800 per batch) for a static neural network …”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In this work, an average of 28 input‐output data pairs was used for each training batch (i.e., sliding window length). This is smaller than the number of data pairs required in the other studies that use the same number of training epochs (i.e., 800 per batch) for a static neural network …”
Section: Discussionmentioning
confidence: 99%
“…This is smaller than the number of data pairs required in the other studies that use the same number of training epochs (i.e., 800 per batch) for a static neural network. 72…”
Section: D Comparison Of Performance With Other Neural Network Modelsmentioning
confidence: 99%
“…This fact has been proven by Chadded [28] by using Gaussian mixture model. Bukovsky et al [29] have used neural network in order to perform identification followed by prediction of tumor. This process is a way advanced to classification process using feedforward neural network.…”
Section: Related Workmentioning
confidence: 99%
“…One application is to support lung movement tracking for the accurate delivery of radiotherapy to lung cancers. The system latency of existing techniques limits their accuracy while real‐time retraining of neural networks has been shown to improve precision of delivery …”
mentioning
confidence: 99%
“…The system latency of existing techniques limits their accuracy while real-time retraining of neural networks has been shown to improve precision of delivery. 9 There are important considerations to ensure the safe and effective implementation of AI into health care. Data collection and usage must follow informed consent.…”
mentioning
confidence: 99%