Original Article
Feasibility study on liver tumor motion prediction based on back propagation neural network
Yao Ye, Ge Weiqiang, Zhou Yun, Zhang Libo
Published 2016-01-25
Cite as Int J Radiat Med Nucl Med, 2016, 40(1): 22-25. DOI: 10.3760/cma.j.issn.1673-4114.2016.01.005
Abstract
ObjectiveThis study was performed to determine the feasibility of liver tumor motion prediction based on back propagation (BP) neural network.
MethodsA liver cancer patient was scanned using X-ray volume imaging, and all breath motion figures were recorded. The tumor was located using an iodized oil mark. The mark motion track was gathered through image processing. A BP model was established based on the marked track. This model was used for tumor prediction. The results were compared with the true mark track.
ResultsAccurate prediction of liver tumor was achieved via BP neural network, with a deviation of less than 1 pixel. However, the predicted value was less accurate at the peak of the breath motion curve, with a deviation of less than 2 pixels.
ConclusionsBP neural network is proposed as a new approach for liver tumor motion prediction. This network is beneficial to enhance the accuracy of liver stereotactic body radiation therapy and real-time adaptive radiation therapy. The proposed approach could be applied clinically.
Key words:
Liver neoplasms; BP neural network; Breath prediction; Image track
Contributor Information
Yao Ye
Department of Radiation Oncology, Huadong Hospital Affliated to Fudan University, Shanghai 200040, China
Ge Weiqiang
Zhou Yun
Zhang Libo