On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

2005 ◽  
Vol 32 (12) ◽  
pp. 3801-3809 ◽  
Author(s):  
Marcus Isaksson ◽  
Joakim Jalden ◽  
Martin J. Murphy
Keyword(s):  
Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Adaptive Neural Network

scholarly journals A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ivo Bukovsky ◽  
Noriyasu Homma ◽  
Kei Ichiji ◽  
Matous Cejnek ◽  
Matous Slama ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Treatment Time ◽  
Lung Tumor ◽  
Computational Time ◽  
Tumor Motion ◽  
Dose Delivery ◽  
Neural Network Approach ◽  
Radiation Beam ◽  
Total Treatment Time

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 with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking

2012 ◽  
Vol 39 (7Part1) ◽  
pp. 4423-4433 ◽  
Author(s):  
Jihyun Yun ◽  
Marc Mackenzie ◽  
Satyapal Rathee ◽  
Don Robinson ◽  
B. G. Fallone
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Tumor Tracking ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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