Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution
Fig 8
Optimization results for parameter [mini-batch, epochs, and standard deviation of domain filter σd] determination.
The signal-difference-to-noise ratio (SDNR) resulting from differences in the initial learning rate [(a): 0.1, (b): 0.01, and (c): 0.001], mini-batch, and epochs in the DE–VM with the very-deep super-resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) are shown. (d) The SDNRs resulting from differences in σd in DE–VM–VDSR are shown. From the results (Figs 7 and 8) of optimization verification, DE–VM–VDSR images were generated by setting the number of iterations to 30, initial learning rate to 0.001, mini-batch size to 128, epochs to 70, and σd to 1.