Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics
This addresses the need for personalized dosimetry in radionuclide therapy by improving accuracy over fast approximations, though it is incremental as it builds on existing imaging and simulation methods.
The study tackled the problem of inaccurate absorbed dose estimation in nuclear medicine diagnostics by developing a deep learning method to estimate dose voxel kernels from density kernels, achieving an intersection-over-union score of 0.86 and a mean squared error of 1.24e-4 on real patient data.
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.