title: An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking.
abstract: OBJECTIVE
  To address practical issues of implementing artificial neural networks (ANN) for lung-tumor motion prediction in MRI-based intrafractional lung-tumor tracking.
  
  
  METHODS
  A feedforward four-layered ANN structure is used to predict future tumor positions. A back-propagation algorithm is used for ANN learning. Adaptive learning is incorporated by continuously updating weights and learning rate during prediction. An ANN training scheme specific for MRI-based tracking is developed. A multiple-ANN structure is developed to reduce tracking failures caused by the lower imaging rates of MRI. We used particle swarm optimization to optimize the ANN structure and initial weights (IW) for each patient and treatment fraction. Prediction accuracy is evaluated using the 1D superior-inferior lung-tumor motions of 29 lung cancer patients for system delays of 120-520 ms, in increments of 80 ms. The result is compared with four different scenarios: (1), (2) ANN structure optimization + with∕without IW optimization, and (3), (4) no ANN structure optimization + with∕without IW optimization, respectively. An additional simulation is performed to assess the value of optimizing the ANN structure for each treatment fraction.
  
  
  RESULTS
  For 120-520 ms system delays, mean RMSE values (ranges 0.0-2.8 mm from 29 patients) of 0.5-0.9 mm are observed, respectively. Using patient specific ANN structures, a 30%-60% decrease in mean RMSE values is observed as a result of IW optimization, alone. No significant advantages in prediction performance are observed, however, by optimizing for each fraction.
  
  
  CONCLUSIONS
  A new ANN-based lung-tumor motion predictor is developed for MRI-based intrafractional tumor tracking. The prediction accuracy of our predictor is evaluated using a realistic simulated MR imaging rate and system delays. For 120-520 ms system delays, mean RMSE values of 0.5-0.9 mm (ranges 0.0-2.8 mm from 29 patients) are achieved. Further, the advantage of patient specific ANN structure and IW in lung-tumor motion prediction is demonstrated by a 30%-60% decrease in mean RMSE values.
authors: Department of Physics, University of Alberta, Edmonton, Alberta, Canada.
authors: Jihyun
authors: J
authors: Yun
authors: Marc
authors: M
authors: Mackenzie
authors: Satyapal
authors: S
authors: Rathee
authors: Don
authors: D
authors: Robinson
authors: B G
authors: BG
authors: Fallone
mesh_terms: D000465:Algorithms; D016477:Artifacts; D019583:Dose Fractionation, Radiation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008175:Lung Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity