  Dual-energy CT (DECT) has been widely investigated to generate more
informative and more accurate images in the past decades. For example,
Dual-Energy Alternating Minimization (DEAM) algorithm achieves sub-percentage
uncertainty in estimating proton stopping-power mappings from experimental 3-mm
collimated phantom data. However, elapsed time of iterative DECT algorithms is
not clinically acceptable, due to their low convergence rate and the tremendous
geometry of modern helical CT scanners. A CNN-based initialization method is
introduced to reduce the computational time of iterative DECT algorithms. DEAM
is used as an example of iterative DECT algorithms in this work. The simulation
results show that our method generates denoised images with greatly improved
estimation accuracy for adipose, tonsils, and muscle tissue. Also, it reduces
elapsed time by approximately 5-fold for DEAM to reach the same objective
function value for both simulated and real data.
