Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 1113 |
| Journal | Medical Physics |
| Volume | 35 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2008 |
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Keywords
- backpropagation
- biomedical imaging
- conjugate gradient methods
- dosimetry
- Gaussian processes
- generalisation (artificial intelligence)
- mean square error methods
- medical computing
- multilayer perceptrons
- radial basis function networks
- radiation therapy
- regression analysis
- tumours
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A comparison of neural network approaches for on-line prediction in IGRT. / Goodband, John H.; Haas, Olivier C.L.; Mills, J.A.
In: Medical Physics, Vol. 35, No. 3, 2008, p. 1113.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A comparison of neural network approaches for on-line prediction in IGRT
AU - Goodband, John H.
AU - Haas, Olivier C.L.
AU - Mills, J.A.
PY - 2008
Y1 - 2008
N2 - Image-guided radiation therapy aims to improve the accuracy of treatment delivery by tracking tumor position and compensating for observed movement. Due to system latency it is sometimes necessary to predict tumor trajectory evolution in order to facilitate changes in beam delivery. Neural networks (NNs) have previously been investigated for predicting future tumor position because of their ability to model non-linear systems. However, no attempt has been made to optimize the NN training algorithms, and no mention has been made of potential errors which can be caused by using NNs for extrapolation purposes. In this work, after giving a brief explanation of NN theory, a comparison is made between 4 different adaptive algorithms for training time-series prediction NNs. New error criteria are introduced which highlight error maxima. Results are obtained by training the NNs using previously published data. A hybrid algorithm combining Bayesian regularization with conjugate-gradient backpropagation is demonstrated to give the best average prediction accuracy, whilst a generalized regression NN is shown to reduce the possibility of isolated large prediction errors.
AB - Image-guided radiation therapy aims to improve the accuracy of treatment delivery by tracking tumor position and compensating for observed movement. Due to system latency it is sometimes necessary to predict tumor trajectory evolution in order to facilitate changes in beam delivery. Neural networks (NNs) have previously been investigated for predicting future tumor position because of their ability to model non-linear systems. However, no attempt has been made to optimize the NN training algorithms, and no mention has been made of potential errors which can be caused by using NNs for extrapolation purposes. In this work, after giving a brief explanation of NN theory, a comparison is made between 4 different adaptive algorithms for training time-series prediction NNs. New error criteria are introduced which highlight error maxima. Results are obtained by training the NNs using previously published data. A hybrid algorithm combining Bayesian regularization with conjugate-gradient backpropagation is demonstrated to give the best average prediction accuracy, whilst a generalized regression NN is shown to reduce the possibility of isolated large prediction errors.
KW - backpropagation
KW - biomedical imaging
KW - conjugate gradient methods
KW - dosimetry
KW - Gaussian processes
KW - generalisation (artificial intelligence)
KW - mean square error methods
KW - medical computing
KW - multilayer perceptrons
KW - radial basis function networks
KW - radiation therapy
KW - regression analysis
KW - tumours
U2 - 10.1118/1.2836416
DO - 10.1118/1.2836416
M3 - Article
VL - 35
SP - 1113
JO - Medical Physics
JF - Medical Physics
SN - 0094-2405
IS - 3
ER -