DataSheet_1_Dose Super-Resolution in Prostate Volumetric Modulated Arc Therapy Using Cascaded Deep Learning Networks.docx

PurposeThis study proposes a cascaded network model for generating high-resolution doses (i.e., a 1 mm grid) from low-resolution doses (i.e., ≥3 mm grids) with reduced computation time.
MethodsUsing the anisotropic analytical algorithm with three grid sizes (1, 3, and 5 mm) and the Acuros XB algorithm with two grid sizes (1 and 3 mm), dose distributions were calculated for volumetric modulated arc therapy plans for 73 prostate cancer patients. Our cascaded network model consisted of a hierarchically densely connected U-net (HD U-net) and a residual dense network (RDN), which were trained separately following a two-dimensional slice-by-slice procedure. The first network (HD U-net) predicted the downsampled high-resolution dose (generated through bicubic downsampling of the baseline high-resolution dose) using the low-resolution dose; subsequently, the second network (RDN) predicted the high-resolution dose from the output of the first network. Further, the predicted high-resolution dose was converted to its absolute value. We quantified the network performance using the spatial/dosimetric parameters (dice similarity coefficient, mean dose, maximum dose, minimum dose, homogeneity index, conformity index, and V95%, V70%, V50%, and V30%) for the low-resolution and predicted high-resolution doses relative to the baseline high-resolution dose. Gamma analysis (between the baseline dose and the low-resolution dose/predicted high-resolution dose) was performed with a 2%/2 mm criterion and 10% threshold.
ResultsThe average computation time to predict a high-resolution axial dose plane was <0.02 s. The dice similarity coefficient values for the predicted doses were closer to 1 when compared to those for the low-resolution doses. Most of the dosimetric parameters for the predicted doses agreed more closely with those for the baseline than for the low-resolution doses. In most of the parameters, no significant differences (p-value of >0.05) between the baseline and predicted doses were observed. The gamma passing rates for the predicted high-resolution does were higher than those for the low-resolution doses.
ConclusionThe proposed model accurately predicted high-resolution doses for the same dose calculation algorithm. Our model uses only dose data as the input without additional data, which provides advantages of convenience to user over other dose super-resolution methods.

### 研究目的
本研究提出一种级联网络模型，可从低分辨率剂量（即网格间距≥3 mm）生成网格间距为1 mm的高分辨率剂量，同时缩短计算时长。

### 研究方法
本研究针对73例前列腺癌患者的容积调强弧疗计划，采用三种网格间距（1、3、5 mm）的各向异性解析算法（anisotropic analytical algorithm），以及两种网格间距（1、3 mm）的Acuros XB算法计算剂量分布。本研究所提出的级联网络模型由分层密集连接U型网络（hierarchically densely connected U-net，HD U-net）与残差密集网络（residual dense network，RDN）构成，二者通过二维逐片训练流程分别完成训练。第一级网络（HD U-net）以低分辨率剂量为输入，预测经双三次下采样得到的基准高分辨率剂量的下采样版本；随后第二级网络（RDN）以第一级网络的输出为输入，生成最终的高分辨率剂量。此外，将预测得到的高分辨率剂量转换为绝对值形式。本研究以基准高分辨率剂量为参照，通过空间/剂量学参数（戴斯相似系数、平均剂量、最大剂量、最小剂量、均匀性指数、适形指数，以及V95%、V70%、V50%与V30%）量化评估网络性能。针对基准剂量与低分辨率剂量/预测高分辨率剂量之间的伽马分析，采用2%/2 mm的评价标准与10%的剂量阈值。

### 研究结果
单张高分辨率轴向剂量平面的预测平均计算时长小于0.02秒。相较于低分辨率剂量，预测剂量的戴斯相似系数更趋近于1。多数预测剂量的剂量学参数与基准剂量的吻合度优于低分辨率剂量。在多数参数中，基准剂量与预测剂量之间未观察到显著差异（p值>0.05）。预测高分辨率剂量的伽马通过率高于低分辨率剂量。

### 研究结论
本研究提出的模型可针对同一种剂量计算算法准确预测高分辨率剂量。本模型仅以剂量数据作为输入，无需额外辅助数据，相较于其他剂量超分辨率方法，为使用者提供了更便捷的应用优势。