| 张海峰,郁艳军,张富利.基于MHA-resunet神经网络的非小细胞肺癌VMAT放疗剂量分布预测研究[J].中华放射医学与防护杂志,2024,44(6):523-530.Zhang Haifeng,Yu Yanjun,Zhang Fuli.Prediction of dose distribution for VMAT radiotherapy in non-small cell lung cancer patients using MHA-resunet[J].Chin J Radiol Med Prot,2024,44(6):523-530 |
| 基于MHA-resunet神经网络的非小细胞肺癌VMAT放疗剂量分布预测研究 |
| Prediction of dose distribution for VMAT radiotherapy in non-small cell lung cancer patients using MHA-resunet |
| 投稿时间：2023-10-22 |
| DOI：10.3760/cma.j.cn112271-20231022-00132 |
| 中文关键词: 非小细胞肺癌 剂量预测 神经网络 多头注意力 |
| 英文关键词:Non-small cell lung cancer Dose prediction Neural network Multi-head attention |
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| 中文摘要: |
| 目的 应用深度学习神经网络高精度预测非小细胞肺癌(NSCLC)患者容积旋转调强放疗(VMAT)计划的剂量分布。方法 基于Res-Unet基础网络引入大核空洞卷积模块和多头注意力(MHA)机制构建了MHA-resunet网络。在此基础上,以随机数表法从上千例接受VMAT放疗NSCLC患者中选取151例患者,以CT图像、计划靶区(PTV)与危及器官(OARs)轮廓作为输入,以剂量分布图作为输出训练神经网络。然后将该网络的性能与常用的几种网络的性能进行比较,通过PTV与OARs内的体素级平均绝对误差(MAE)和临床剂量体积指标误差对网络性能进行评估。结果 基于MHA-resunet网络的预测剂量与真实计划剂量的平均绝对误差在靶区内为1.51 Gy,靶区的D98、D95误差均<1 Gy。与Res-Unet、Atten-Unet、DCNN 3种常用网络比较,MHA-resunet在靶区与除心脏外的OARs内的剂量误差均为最小。结论 MHA-resunet网络通过提高感受野来学习靶区与危及器官的相对位置关系,能够准确地预测接受VMAT放疗的NSCLC患者的剂量分布。 |
| 英文摘要: |
| Objective To apply deep neural networks to predict high-precision dose distribution in volume modulated arc therapy (VMAT) plans for non-small cell lung cancer (NSCLC) patients. Methods This study developed a U-shaped network called MHA-resunet, which incorporated a large kernel dilated convolution module and a multi-head attention module. The model was trained from 151 VMAT plans of NSCLC patients. CT images, planning target volume (PTV) and organs at risk (OARs) were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks'performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics. Results The MAE between the dose distribution predicted by MHA-resunet network and the manually planned dose distribution within the PTV area was 1. 51 Gy, and the D98 and D95 errors in the PTV area were both < 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-resunet was the smallest in the PTV area and in OARs except for the heart. Conclusions The proposed MHA-resunet network improves the receptive field to learn the relative spatial relationship between the PTV area and the OARs, enabling accurate prediction of dose distribution in NSCLC patients undergoing VMAT radiotherapy. |
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