  Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio
(SPR) map with higher accuracy than conventional single energy CT (SECT) by
obtaining the energy dependence of photon interactions. However, DECT is not as
widely implemented as SECT in proton radiation therapy simulation. This work
presents a learning-based method to synthetize DECT images from SECT for proton
radiation therapy. Methods: The proposed method uses a residual attention
generative adversarial network. Residual blocks with attention gates were used
to force the model focus on the difference between DECT maps and SECT images.
To evaluate the accuracy of the method, we retrospectively investigated 20
head-and-neck cancer patients with both DECT and SECT scans available. The high
and low energy CT images acquired from DECT acted as learning targets in the
training process for SECT datasets and were evaluated against results from the
proposed method using a leave-one-out cross-validation strategy. To evaluate
our method in the context of a practical application, we generated SPR maps
from sDECT using physics-based dual-energy stoichiometric method and compared
the maps to those generated from DECT. Results: The synthesized DECT images
showed an average mean absolute error around 30 Hounsfield Unit (HU) across the
whole-body volume. The corresponding SPR maps generated from synthetic DECT
showed an average normalized mean square error of about 1% with reduced noise
level and artifacts than those from original DECT. Conclusions: The accuracy of
the synthesized DECT image by our machine-learning-based method was evaluated
on head and neck patient, and potential feasibility for proton treatment
planning and dose calculation was shown by generating SPR map using the
synthesized DECT.
