Fig 1.
Flowchart of the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) and Bilateral Filtering (BF).
DE–VM–VDSR with BF is implemented by combining preprocessing [VM and VDSR with unsharp masking (UM)] and postprocessing (BF).
Fig 2.
Overview and interrelation of each algorithm.
The relation between very-deep super-resolution (VDSR) with unsharp masking (UM), the simultaneous algebraic reconstruction technique total-variation first-iterative shrinkage–thresholding algorithm (SART–TV–FISTA), and bilateral filtering (BF), which are the cores of the reconstruction process.
Fig 3.
Linear and mass attenuation coefficients for each energy.
The linear attenuation coefficients of a simulated ground-glass opacity (GGO) nodule, a soft tissue, and an artificial bone with respect to the photon energy.
Fig 4.
Convolutional neural network for super-resolution chest digital tomosynthesis.
Fig 5.
Differences in simulated GGO nodule detection on polychromatic and Virtual Monochromatic (VM) projection-based images.
The visibility of the simulated GGO nodule in the projection image is improved by performing UM. The upper row shows the projection image (with/without UM), whereas the lower row shows the global image threshold using Otsu’s method.
Fig 6.
Assessing improvements in image quality using the Signal-Difference-to-Noise Ratio (SDNR), a statistical model with a Gumbel distribution, and the radial Modulation Transfer Function (radial MTF) of the selected features.
(a) The in-focus plane image shows the simulated ground-glass opacity (GGO) nodule and background areas of the SDNR, (b) measurements and the ripple artifact with simulated GGO nodule areas of the Gumbel analysis, and (c) measurements of the simulated GGO nodule areas of the radial MTF.
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.
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.
Fig 9.
Comparisons among the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and conventional reconstruction algorithms [DE–VM–VDSR with and without BF (showing window: 0–0.23), FBP (kernel: Ramp; 0–0.4), SART (120 kV; 0–0.02), SART–TV–FISTA (120 kV; 0–0.4), and DE–VM–SART–TV–FISTA (60 keV; 0–0.23)] in the in-focus plane.
The window of the chest phantom N1 with lung field was varied to compare the contrast and background gray levels. For each corresponding set, the VM (DE–VM–VDSR and DE–VM–SART–TV–FISTA) images are displayed at the same window width and level, whereas the polychromatic FBP and IR images have larger window widths because the backgrounds are less flattened. The X-ray source is moved vertically along the image. Abbreviations: FBP = filtered backprojection, SART = simultaneous algebraic reconstruction technique, TV–FISTA = total-variation first-iterative shrinkage–thresholding algorithm, IR = iterative reconstruction, BF = bilateral filtering.
Fig 10.
Plots of the SDNR versus each algorithm from the in-focus plane.
Comparisons of the signal-difference-to-noise ratio (SDNR) of in-focus plane images obtained via the dual-energy (DE) virtual monochromatic (VM) with the very-deep super-resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and the conventional reconstruction algorithms.
Fig 11.
The largest variations extracted from 23 pixel-value profiles are plotted.
The relatively large variations in pixel values were attributed to high-frequency ripple artifacts.
Fig 12.
Plots of the radial Modulation Transfer Function (radial MTF) versus spatial frequency (cycles/mm) from the in-focus plane for the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and conventional reconstruction algorithms.
The spatial frequency of the simulated ground-glass opacity (GGO) nodule area indicates the simulated GGO nodule and background areas of the radial MTF measurements. The plots are of the area of the GGO nodule and background regions.
Fig 13.
Plots of the Noise Power Spectrum (NPS) versus spatial frequency (cycles/mm) from the in-focus plane for the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and conventional reconstruction algorithms.
The NPS was obtained via two-dimensional Fourier analysis of the central field of view from the reconstructed image of the uniform water phantom. (a) Horizontal directions and (b) vertical directions.