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Proton therapy requires accurate dose calculation for treatment planning to ensure conformal doses are precisely delivered to tumor target. This study proposes a fully connected neural network (FCNN) to figure out the underlying correlation between dual-energy computed tomography (DECT) parametric maps, material mass density and stopping power ratio (SPR) maps, which are essential for proton analytical and Monte Carlo dose calculations. A Siemens SOMATOM Definition Edge scanner (Siemens Medical Solutions, Germany) was used to acquire the DECT images with TwinBeam protocols. The effective atomic number maps, electron density maps, and virtual mono-energetic images were derived by Siemens Syngo.Via. The proposed FCNN includes 9 hidden layers, 200 hidden units, and nonlinear activation functions with layer normalization to prevent gradient vanishing issues of deep neural networks. The model was trained using multiple scanned data from a CIRS electron density phantom 062M with different inserts (Computerized Imaging Reference Systems, Inc., Norfolk, VA) and tested using a CIRS M701 anthropomorphic phantom. For the anthropomorphic phantom, the relative mean absolute errors of the density map were 0.45%, 0.77%, 0.23%, and 1.15% for lung, soft tissue, brain, and bone, while the SPR errors were 0.7%, 1.59%, 1.56%, and 1.23%, respectively. The results indicated that FCNN-based DECT parametric mapping generated robust and accurate mass density and SPR maps with minimum impacts by CT noise due to the usage of millions of training data. The proposed FCNN-based method potentially can improve proton range uncertainty by offering accurate material properties converted from DECT, especially for lung and bone.
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