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Dual-energy CT (DECT) has been widely investigated to generate moreinformative and more accurate images in the past decades. For example,Dual-Energy Alternating Minimization (DEAM) algorithm achieves sub-percentageuncertainty in estimating proton stopping-power mappings from experimental 3-mmcollimated phantom data. However, elapsed time of iterative DECT algorithms isnot clinically acceptable, due to their low convergence rate and the tremendousgeometry of modern helical CT scanners. A CNN-based initialization method isintroduced to reduce the computational time of iterative DECT algorithms. DEAMis used as an example of iterative DECT algorithms in this work. The simulationresults show that our method generates denoised images with greatly improvedestimation accuracy for adipose, tonsils, and muscle tissue. Also, it reduceselapsed time by approximately 5-fold for DEAM to reach the same objectivefunction value for both simulated and real data.

A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapy