title: 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 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.
authors.affiliation: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic.
authors.forename: Ivo
authors.identifier: 0000-0002-7715-2841
authors.initials: I
authors.lastname: Bukovsky
authors.affiliation: Department of Radiological Imaging and Informatics, Graduate School of Medicine, Tohoku University, Sendai 980-8575, Japan.
authors.forename: Noriyasu
authors.identifier: 0000-0001-7543-6181
authors.initials: N
authors.lastname: Homma
authors.affiliation: Division on Advanced Information Technology, Yoshizawa Laboratory, Tohoku University, Sendai 980-8578, Japan.
authors.forename: Kei
authors.identifier: 0000-0002-0072-1399
authors.initials: K
authors.lastname: Ichiji
authors.affiliation: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic.
authors.forename: Matous
authors.identifier: 0000-0001-9130-7988
authors.initials: M
authors.lastname: Cejnek
authors.affiliation: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic.
authors.forename: Matous
authors.initials: M
authors.lastname: Slama
authors.affiliation: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Benes
authors.affiliation: Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic.
authors.forename: Jiri
authors.initials: J
authors.lastname: Bila
mesh_terms: D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology* / Q000503:physiopathology* / Q000532:radiotherapy; D008954:Models, Biological; D009038:Motion; D016571:Neural Networks, Computer; D015656:Respiratory Mechanics