Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution
Fig 7
Optimization results for parameter [number of iterations for Iterative Reconstruction (IR) and Dual-Energy Virtual Monochromatic (DE–VM) energy] determination.
(a) The root-mean square error (RMSE) with respect to number of iterations are shown for each polychromatic IR algorithm. (b) The signal-difference-to-noise ratios (SDNR) resulting from differences in the DE–VM energy in the simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage–thresholding algorithm (FISTA) [DE–VM–SART–TV–FISTA] are shown.
