The distribution of absorbed dose in radionuclide therapy with Lu$^{177}$ can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but inaccurate approximation is unsuitable for personalised dosimetry because it neglects tissue heterogeneity. Such heterogeneity can be incorporated by combining imaging modalities such as computed tomography and single-photon emission computed tomography with computationally expensive Monte Carlo simulation. The aim of this study is to estimate, for the first time, dose voxel kernels from density kernels derived from computed-tomography data by means of deep learning using convolutional neural networks. On a test set of real patient data, the proposed architecture achieved an intersection-over-union score of $0.86$ after $308$ epochs and a corresponding mean squared error of $1.24\times 10^{-4}$. This generalisation performance shows that the trained convolutional network is indeed capable of learning the map from density kernels to dose voxel kernels. Future work will evaluate dose voxel kernels estimated by neural networks against Monte Carlo simulations of whole-body computed tomography in order to predict patient-specific voxel dose maps.

翻译：在Lu$^{177}$放射性核素治疗中，吸收剂量的分布可通过将时间积分活性分布图像与代表不同组织类型的剂量体素核进行卷积来近似。这种快速但不精确的近似方法因忽略了组织异质性而不适用于个体化剂量学。通过将计算机断层扫描和单光子发射计算机断层扫描等影像模态与计算昂贵的蒙特卡洛模拟相结合，可以纳入这种异质性。本研究旨在首次利用卷积神经网络，通过深度学习从计算机断层扫描数据导出的密度核来估算剂量体素核。在真实患者数据测试集上，所提出的架构在$308$个训练周期后实现了$0.86$的交并比分数，相应的均方误差为$1.24\times 10^{-4}$。这一泛化性能表明，训练后的卷积神经网络确实能够学习从密度核到剂量体素核的映射关系。未来工作将评估由神经网络估算的剂量体素核与全身计算机断层扫描蒙特卡洛模拟的对比结果，以预测患者特异性体素剂量图。