Machine Learning Based Oxygen and Carbon Concentration Derivation Using Dual-Energy CT for PET-Based Dose Verification in Proton Therapy
Y Liu1*, L Zhou2, H PENG1,3, M jia4, (1) Wuhan University, Wuhan, China, 430072 (2) Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China (3) ProtonSmart Ltd, Wuhan, China, 430072 (4) Tianjin university, Tianjin, China 300072
Presentations
SU-J-BRC-8 (Sunday, 7/10/2022) 4:00 PM - 5:00 PM [Eastern Time (GMT-4)]
Ballroom C
Purpose: Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g. C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT).
Methods: DECT images were synthesized based on digital patient phantoms at the head site using two
methods: 1) theoretical CT numbers with Gaussian noise (method 1) and 2) forward/backward image reconstruction with poly-energetic energy spectrum and Poisson noise added in the projection domain (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights under different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution in proton therapy.
Results: When no additional noise present, all four cases can obtain <2% mean absolute errors (MAE) and <4% root mean square error (RMSE). Both models demonstrate good performance even with the presence of noise. The activity profiles exhibit 3-5% difference in terms of mean relative error (MRE) between the ground truth and the machine learning outcome.
Conclusion: We explored the feasibility of machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Compared to conventional methods based on either SECT or DECT, a machine learning framework is advantageous on the following two aspects: geometric prior and noise/artifact immunity. This study lays a foundation for us to apply the proposed approach to clinical DECT images.
Keywords
Dual-energy Imaging, Dosimetry
Taxonomy
IM- CT: Dual Energy and Spectral
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