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The distribution of energy dose from Lu$^{177}$ radiotherapy can be estimatedby convolving an image of a time-integrated activity distribution with a dosevoxel kernel (DVK) consisting of different types of tissues. This fast andinacurate approximation is inappropriate for personalized dosimetry as itneglects tissue heterogenity. The latter can be calculated using differentimaging techniques such as CT and SPECT combined with a time consumingmonte-carlo simulation. The aim of this study is, for the first time, anestimation of DVKs from CT-derived density kernels (DK) via deep learning inconvolutional neural networks (CNNs). The proposed CNN achieved, on the testset, a mean intersection over union (IOU) of $= 0.86$ after $308$ epochs and acorresponding mean squared error (MSE) $= 1.24 \cdot 10^{-4}$. Thisgeneralization ability shows that the trained CNN can indeed learn thedifficult transfer function from DK to DVK. Future work will evaluate DVKsestimated by CNNs with full monte-carlo simulations of a whole body CT topredict patient specific voxel dose maps.

Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics