  Low-dose computed tomography (CT) allows the reduction of radiation risk in
clinical applications at the expense of image quality, which deteriorates the
diagnosis accuracy of radiologists. In this work, we present a High-Quality
Imaging network (HQINet) for the CT image reconstruction from Low-dose computed
tomography (CT) acquisitions. HQINet was a convolutional encoder-decoder
architecture, where the encoder was used to extract spatial and temporal
information from three contiguous slices while the decoder was used to recover
the spacial information of the middle slice. We provide experimental results on
the real projection data from low-dose CT Image and Projection Data
(LDCT-and-Projection-data), demonstrating that the proposed approach yielded a
notable improvement of the performance in terms of image quality, with a rise
of 5.5dB in terms of peak signal-to-noise ratio (PSNR) and 0.29 in terms of
mutual information (MI).
