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Development of a novel algorithm to improve image quality in chest digital tomosynthesis using convolutional neural network with super-resolution

We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) reconstruction algorithm (DVV) that uses projection data to improve nodule contrast and resolution during chest digital tomosynthesis (CDT). To estimate residual errors in high-resolution and multiscale VM images in projection space, the DVV algorithm employs a training network (mini-batch stochastic gradient descent algorithm with momentum) that involves subjectively reconstructed hybrid SR images [simultaneous algebraic reconstruction technique (SART) total variation (TV)-first iterative shrinkage-thresholding algorithm (FISTA); SART-TV-FISTA]. DE-DT imaging was accomplished using pulsed X-ray exposures that were rapidly switched between low- and high-tube potential kVp, followed by image comparisons using conventional polychromatic filtered back projection (FBP), SART-TV-FISTA, and DE-VM-SART-TV-FISTA algorithms. Improvements in contrast and resolution were compared using signal difference- to-noise ratio (SDNR) and radial-modulation transfer function (radial-MTF) of a chest phantom with simulated ground-glass opacity (GGO) nodules. The novel DVV algorithm improved overall performance in terms of SDNR and yielded high quality images independent of the type of simulated GGO nodules used in the chest phantom. The novel DVV algorithm yielded superior resolution in comparison with conventional reconstruction algorithms for the radial-MTF analysis, with and without VM processing. Furthermore, the DVV algorithm improved contrast and spatial resolution.