| 基于顺序嵌入结合关联嵌入的呼吸运动预测 |
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| 引用本文： | 张昆鹏,于佳弘,靳 爽,苏 哲,徐晓桐,张 华.基于顺序嵌入结合关联嵌入的呼吸运动预测[J].南方医科大学学报,2022,42(12):1858-1866. |
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| 作者姓名： | 张昆鹏 于佳弘 靳 爽 苏 哲 徐晓桐 张 华 |
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| 作者单位： | 南方医科大学生物医学工程学院，广东省医学图像处理重点实验室，广东省医学影像与诊断技术工程实验室，广东 广州 510515 |
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| 摘 要： | 目的 提出一个深度学习模型实现呼吸运动各个方向一体化的建模预测。方法 将不同方向的呼吸运动信号分别输入由LSTM组成的顺序嵌入层去捕获历史运动状态的顺序依赖，得到顺序嵌入表示。顺序嵌入表示通过自注意力机制实现各个方向的关联嵌入，得到关联嵌入表示。将顺序嵌入表示和关联嵌入表示进行拼接输入由全连接神经网络组成的预测层生成非线性预测分量，并于平行于上述结构的自回归模块生成的线性预测分量相加生成最终的预测。模型的训练采用“预训练+微调”的模式。在我们的实验中，304例呼吸运动轨迹被使用进行模型预训练，7例测评样本被使用进行模型的测试。结果 所提出预测模型相比于其他比较方法取得更准确的预测效果，在7例测评样本不同延迟时间上的3D方向绝对偏差减小平均达70%以上。结论 提出模型在解决精准放疗过程中的系统延迟问题有很大的应用价值，能提供精准的位置预测。
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| 关 键 词： | 放射治疗 呼吸运动 深度学习 顺序嵌入 关联嵌入 |
Prediction of respiratory motion based on sequential embedding combined with relational embedding |
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| ZHANG Kunpeng,YU Jiahong,JIN Shuang,SU Zhe,XU Xiaotong,ZHANG Hua.Prediction of respiratory motion based on sequential embedding combined with relational embedding[J].Journal of Southern Medical University,2022,42(12):1858-1866. |
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| Authors: | ZHANG Kunpeng YU Jiahong JIN Shuang SU Zhe XU Xiaotong ZHANG Hua |
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| Affiliation: | School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Provincial Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China |
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| Abstract: | Objective To propose a deep learning model for modeling and prediction of the integration of respiratory motion in all directions. Methods The respiratory motion signals in different directions were input into the sequential embedding layer composed of LSTM to capture the sequential dependence of the historical motion state and obtain the sequential embedding representation, which enabled relational embedding in all directions through the self-attention mechanism to obtain the relational embedding representation. The sequential embedding representation and the relational embedding representation were concatenated and input into a prediction layer consisting of a fully connected neural network to generate nonlinear prediction components, which were added to the linear prediction components generated by the autoregressive module parallel to the above structure to generate the final prediction. The model was trained using a 'pre-training + fine-tuning' approach. In the validation experiments, 304 respiratory motion trajectories were used for model pre-training, and 7 evaluation samples were used for model testing. Results The proposed prediction model achieved more accurate prediction results than other methods. For the 7 evaluation samples with different delay time, the proposed prediction model achieved a reduction of absolute deviations in the 3D directions by over 70% . Conclusion The proposed model is capable of accurate prediction of respiratory motion and can thus help to reduce system delay in precise radiotherapy |
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| Keywords: | radiotherapy respiratory motion deep learning sequential embedding relational embedding |
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