Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics

The distribution of energy dose from Lu$^{177}$ radiotherapy can be estimated by convolving an image of a time-integrated activity distribution with a dose voxel kernel (dvk) consisting of different types of tissues. This fast and inacurate approximation is inappropriate for personalized dosimetry as it neglects tissue heterogenity. The latter can be calculated using different imaging techniques such as CT and SPECT combined with a time consuming monte-carlo simulation. The aim of this study is, for the first time, an estimation of DVKs from CT-derived density kernels (dk) via deep learning in convolutional neural networks (cnns). The proposed cnn achieved, on the test set, a mean intersection over union (iou) of $= 0.86$ after $308$ epochs and a corresponding mean squared error (mse) $= 1.24 \cdot 10^{-4}$. This generalization ability shows that the trained cnn can indeed learn the complex transfer function from dk to dvk. Future work will evaluate dvks estimated by cnns with full monte-carlo simulations of a whole body CT to predict patient specific voxel dose maps. Keywords: Deep Learning, Nuclear Medicine, Diagnostics, Machine Learning, Statistics