Publication

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method

Downloadable Content

Persistent URL
Last modified
  • 07/21/2025
Type of Material
Authors
    Yuan Gao, Emory UniversityChih-Wei Chang, Emory UniversityJustin Roper, Emory UniversityMarian Axente, Emory UniversityYang Lei, Emory UniversityShaoyan Pan, Emory UniversityJeffrey D. Bradley, University of PennsylvaniaJun Zhou, Emory UniversityTian Liu, Emory UniversityXiaofeng Yang, Emory University
Language
  • English
Date
  • 2023-11-23
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2023 Gao, Chang, Roper, Axente, Lei, Pan, Bradley, Zhou, Liu and Yang
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 13
Start Page
  • 1278180
Grant/Funding Information
  • The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research is supported in part by the National Institutes of Health under Award Number R01CA215718, R56EB033332, and R01EB032680.
Abstract
  • Background The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT. Objectives The purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment. Methods Artificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models. Results For M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%. Conclusion The results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation. Advances in knowledge Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.
Author Notes
Keywords

Tools

Relations

In Collection:

Items

Thumbnail Title File Description Date Uploaded Visibility Actions
file details Publication File - wc8c0.pdf Primary Content 2025-06-11 Public Download