Spatial resolution is of paramount importance for intracranial vessel wall (IVW) MR imaging because the vessel wall is submillimeter thin. However, high spatial resolution typically comes at the expense of long scan time, small spatial coverage and low signal-to-noise ratio (SNR). If we can reconstruct a high-resolution (HR) image from a low-resolution (LR) input, we can potentially achieve larger spatial coverage, higher SNR and better spatial resolution in a shorter scan. In this work, we propose a new Single Image Super-Resolution(SISR) technique which recovers an HR image from an LR image using 3D Densely Connected Super-Resolution Networks (DCSRN). We compared our network with state-of-art deep learning network in restoring 4x down-graded images and ours are faster and better.
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