  Purpose: Dual-energy CT (DECT) has been used to derive relative stopping
power (RSP) map by obtaining the energy dependence of photon interactions. The
DECT-derived RSP maps could potentially be compromised by image noise levels
and the severity of artifacts when using physics-based mapping techniques,
which would affect subsequent clinical applications. This work presents a
noise-robust learning-based method to predict RSP maps from DECT for proton
radiation therapy. Methods: The proposed method uses a residual attention
cycle-consistent generative adversarial (CycleGAN) network. CycleGAN were used
to let the DECT-to-RSP mapping be close to a one-to-one mapping by introducing
an inverse RSP-to-DECT mapping. We retrospectively investigated 20
head-and-neck cancer patients with DECT scans in proton radiation therapy
simulation. Ground truth RSP values were assigned by calculation based on
chemical compositions, and acted as learning targets in the training process
for DECT datasets, and were evaluated against results from the proposed method
using a leave-one-out cross-validation strategy. Results: The predicted RSP
maps showed an average normalized mean square error (NMSE) of 2.83% across the
whole body volume, and average mean error (ME) less than 3% in all volumes of
interest (VOIs). With additional simulated noise added in DECT datasets, the
proposed method still maintained a comparable performance, while the
physics-based stoichiometric method suffered degraded inaccuracy from increased
noise level. The average differences in DVH metrics for clinical target volumes
(CTVs) were less than 0.2 Gy for D95% and Dmax with no statistical
significance. Conclusion: These results strongly indicate the high accuracy of
RSP maps predicted by our machine-learning-based method and show its potential
feasibility for proton treatment planning and dose calculation.
