Abstract：
Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.
摘要：
调强放射治疗（IMRT）已广泛用于治疗头颈部肿瘤。然而,由于头颈部复杂的解剖结构，对于计划优化器而言,快速生成临床上可接受的IMRT治疗计划具有挑战性.在当前研究中开发了一种新颖的深度学习多尺度变换器（MST）模型，旨在加速头颈部肿瘤的IMRT计划，同时生成更精确的体素水平剂量分布预测。提出的端到端MST模型采用分流变压器来捕获多尺度特征并学习全局依赖关系，并利用3D可变形卷积瓶颈块来提取形状感知特征并补偿补丁合并层中空间信息的损失。此外，数据扩充和自我知识提炼,进一步提高了模型的预测性能。在OpenKBP挑战数据集上对MST模型进行了训练和评估。将其预测精度与以前的三种剂量预测模型进行了比较：C3D，TrDosePred,和TSNet。我们提出的MST模型在肿瘤区域中的预测剂量分布最接近原始临床剂量分布。MST模型在测试数据集上达到2.23Gy的剂量评分和1.34Gy的DVH评分,跑赢其他三种模式8%-17%。对于临床相关的DVH剂量学指标，D99的平均绝对误差(MAE)预测精度为2.04%，D95为1.54%，D1为1.87%，D平均值为1.87%，D0.1cc为1.89%，分别,优于其他三个模型。定量结果表明，与先前的头颈部肿瘤模型相比，所提出的MST模型实现了更准确的体素水平剂量预测。MST模型具有很大的应用潜力，可以应用于其他疾病部位，以进一步提高放疗计划的质量和效率。
