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

Yuan Gao; Chih-Wei Chang; Shaoyan Pan; Yang Lei; Tonghe Wang; Jun Zhou; Jeffrey D. Bradley; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2654016

7 April 2023 Mass density estimation based on single-energy computed tomography via deep learning

Yuan Gao, Chih-Wei Chang, Shaoyan Pan, Yang Lei, Tonghe Wang, Jun Zhou, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124632X (2023) https://doi.org/10.1117/12.2654016
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Yuan Gao, Chih-Wei Chang, Shaoyan Pan, Yang Lei, Tonghe Wang, Jun Zhou, Jeffrey D. Bradley, Tian Liu, and Xiaofeng Yang "Mass density estimation based on single-energy computed tomography via deep learning", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124632X (7 April 2023); https://doi.org/10.1117/12.2654016

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