Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
Geneva University Neurocenter, Geneva University, CH_1205, Geneva, Switzerland.
Eur Radiol. 2023 Dec;33(12):9411-9424. doi: 10.1007/s00330-023-09839-y. Epub 2023 Jun 27.
We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.
The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed.
The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was - 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, - 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were - 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, - 1.11 ± 2.90%, 2.34 ± 2.03%, respectively.
Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation.
We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations.
• We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.
我们提出了一种基于深度学习的方法，从全身 CT 采集生成体素剂量图。
使用蒙特卡罗（MC）模拟考虑患者和扫描仪特定特征（SP_MC）计算与每个源位置/角度相对应的体素剂量图。通过 MC 计算计算均匀圆柱内的剂量分布（SP_uniform）。将密度图和 SP_uniform 剂量图输入到残差深度神经网络（DNN）中，通过图像回归任务预测 SP_MC。通过有/无管电流调制（TCM）的转移学习，在使用两种管电压扫描的 11 个测试案例中比较 DNN 和 MC 重建的全身剂量图。进行体素和器官剂量评估，例如平均误差（ME，mGy）、平均绝对误差（MAE，mGy）、相对误差（RE，%）和相对绝对误差（RAE，%）。
对于 120 kVp 和 TCM 测试集，在 ME、MAE、RE 和 RAE 体素参数方面，模型性能为-0.0302±0.0244 mGy、0.0854±0.0279 mGy、-1.13±1.41%和 7.17±0.44%。在 ME、MAE、RE 和 RAE 器官参数方面，在所有分段器官上，120 kVp 和 TCM 场景的器官误差平均为-0.144±0.342 mGy 和 0.23±0.28 mGy、-1.11±2.90%、2.34±2.03%。
我们提出的深度学习模型能够从全身 CT 扫描中以合理的精度生成体素水平的剂量图，适合器官水平的吸收剂量估计。
我们提出了一种使用深度神经网络计算体素剂量图的新方法。与冗长的蒙特卡罗计算相比，这种方法在可接受的计算时间内为患者进行准确的剂量计算具有临床相关性。
• 我们提出了一种替代蒙特卡罗剂量计算的深度神经网络方法。• 我们提出的深度学习模型能够从全身 CT 扫描中以合理的精度生成体素水平的剂量图，适合器官水平的剂量估计。• 通过从单个源位置生成剂量分布，我们的模型可以为广泛的采集参数生成准确和个性化的剂量图。
