| BP神经网络模型预测肝肿瘤运动趋势可行性研究 |
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| 引用本文： | 姚晔,戈伟强,周云,张立波. BP神经网络模型预测肝肿瘤运动趋势可行性研究[J]. 国际放射医学核医学杂志, 2016, 40(1): 22-25. DOI: 10.3760/cma.j.issn.1673-4114.2016.01.005 |
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| 作者姓名： | 姚晔 戈伟强 周云 张立波 |
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| 作者单位： | 200040 上海, 复旦大学附属华东医院放疗科 |
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| 摘 要： | 目的 利用BP(back propagation)神经网络模型对1例肝癌碘油介入术后患者的肝肿瘤运动趋势进行预测。 方法 使用X射线容积成像系统对某肝癌患者进行扫描, 采集各时相呼吸运动图像。利用碘油标记方法, 对肝癌病灶进行定位, 并通过图像检测技术, 获取病灶标记点的运动轨迹。对标记点的运动轨迹数据进行分析, 建立BP神经网络模型, 并用其预测下一时间段的运动曲线, 将预测结果与肿瘤标记点实际的运动轨迹进行比较分析。 结果 利用BP神经网络可以有效预测肝肿瘤的运动趋势, 在一定时间段内可保持良好的精准度, 误差在1个像素距离内, 但在呼吸运动峰值处预测精准度尚不理想, 误差接近2个像素距离。 结论 BP神经网络模型是预测肝肿瘤运动的一种新方法, 可能对肝癌的体部立体定向放疗以及实时跟踪放疗精准度的提升有一定帮助, 且具有一定的临床价值。
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| 关 键 词： | 肝肿瘤 BP神经网络 呼吸预测 图像跟踪 |
| 收稿时间： | 2015-07-19 |
Feasibility study on liver tumor motion prediction based on back propagation neural network |
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| Ye Yao,Weiqiang Ge,Yun Zhou,Libo Zhang. Feasibility study on liver tumor motion prediction based on back propagation neural network[J]. International Journal of Radiation Medicine and Nuclear Medicine, 2016, 40(1): 22-25. DOI: 10.3760/cma.j.issn.1673-4114.2016.01.005 |
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| Authors: | Ye Yao Weiqiang Ge Yun Zhou Libo Zhang |
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| Affiliation: | Department of Radiation Oncology, HuadongHospital Affliated to Fudan University, Shanghai 200040, China |
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| Abstract: | Objective This study was performed to determine the feasibility of liver tumor motion prediction based on back propagation(BP) neural network.Methods A 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.Results Accurate 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.Conclusions BP 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. |
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| Keywords: | Liver neoplasms BP neural network Breath prediction Image track |
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