Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy

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: Digital phantoms at the head site were constructed based on single-energy CT (SECT) and stoichiometric calibration. DECT images (80 and 140 kVp) were synthesized 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 modeled (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights. Four cases (UNet-1: Method 1+UNet, ResNet-1: Method 1+ResNet, UNet-2: Method 2+UNet, and ResNet-2: Method 2 +ResNet) were tested for different tissue types and different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution. Results: When no noise is present, all four cases are able to obtain <2% mean absolute errors and <4% root mean square error (RMSE). For the worst image quality (e.g., lowest image SNR), the RMSE for O among all tissue types is 3.02% (UNet-1), 4.46% (ResNet-1), 4.38% (UNet-2), and 6.31% (ResNet-2), respectively. For UNet-1 and ResNet-1, the model performed slightly better in terms of RMSE for skeletal tissue than soft tissues. Such a trend is not observed for UNet-2 and ResNet-2. With regard to the comparison between UNet and ResNet, different accuracy and noise immunity are observed. The activity profiles exhibit 3%–5% difference in terms of mean relative error between the ground truth and machine learning outcome. Conclusion: We explored the feasibility of a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. This study lays a foundation for us to apply the proposed approach to clinical DECT images.