Mass density estimation based on single-energy computed tomography via deep learning

Abstract

Proton therapy requires highly accurate dose calculation for treatment planning to ensure the doses delivered to the tumor precisely. The accuracy of mass density estimation dominates the uncertainty in proton dose calculation. This work proposed a fully connected neural network (FCNN) based framework to estimate mass density from single-energy compute tomography. The FCNN was design as 9 hidden layers and 150 hidden units and nonlinear activation function. A CIRS 062M electron density phantom was used to train FCNN, and CIRS M701 and M702 was used to evaluate the performance of models. For M701, FCNN has mean absolute percentage errors of mass density at 0.39%,0.92%,0.68%,1.57,0.92% over brain, spinal cord, soft tissue, lung, and bone. For M702, the mean absolute percentage errors of mass density estimation by FCNN are 0.89%,1.09%,0.70%,1.52% and 3.19%, respectively.