Mass density estimation based on single-energy computed tomography via deep learning Conference Paper
| Authors: | Gao, Y.; Chang, C. W.; Pan, S.; Lei, Y.; Wang, T.; Zhou, J.; Bradley, J. D.; Liu, T.; Yang, X. |
| Title: | Mass density estimation based on single-energy computed tomography via deep learning |
| Conference Title: | Medical Imaging 2023: Physics of Medical Imaging |
| 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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. |
| Keywords: | computerized tomography; medical imaging; energy; dose calculation; proton therapy; highly accurate; proton beams; deep learning; proton beam therapy; mass density; sect; density estimation; fully connected neural network; mass densities; percentage error |
| Journal Title | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Volume: | 12463 |
| Conference Dates: | 2023 Feb 19-23 |
| Conference Location: | San Diego, CA |
| ISBN: | 1605-7422 |
| Publisher: | SPIE |
| Date Published: | 2023-01-01 |
| Start Page: | 12463-112 |
| Language: | English |
| DOI: | 10.1117/12.2654016 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference paper: 12463 2X -- Source: Scopus |
