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CT systems with large detector size suffer from lower z-resolution leading to pixelated images and inability to detect small structures thus adversely impacting the diagnosis and screening. Overlap reconstruction can partially reduce the stair-step artifacts but does not improve the effect of wider slice sensitivity profile (SSP) and thus continues to have reduced visibility of smaller structures. In this work, we propose a supervised deep learning method for z-resolution enhancement such that (a) the effective SSP of resulting image is reduced, (b) quantitative values of tissue (CT numbers) and tissue-contrast are preserved; (c) very limited noise enhancement and (d) improved tissue interface in bone/soft tissue. The proposed method devises a super resolution (SURE) network which is trained to map the low resolution (LR) slices to the corresponding high resolution (HR) slices. A 2D network is trained with sagittal and coronal slices with the LR-HR pair sets. The training is performed using ground truth HR slices obtained from high end systems, and the corresponding LR slices are synthesized by either using retro reconstruction with higher slice thickness and spacing or through averaging of slices in z-direction from HR images. The network is trained with both these types of images with helical acquisition volumes from a range of scanners. Qualitative and quantitative analysis is done on the predicted HR images and compared with the original HR images. FWHM for SSP of the predicted HR images reduced from ~0.98 to ~0.73, when the target was 0.64, thus improving the real z-resolution. HU distribution of different tissue types also showed stability in terms of mean value. Noise measured through standard deviation was slightly higher than the LR image but lower than that of original HR images. PSNR also showed consistent improvement on all the cases across 3 different systems.
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