  Limited view tomographic reconstruction aims to reconstruct a tomographic
image from a limited number of sinogram or projection views arising from sparse
view or limited angle acquisitions that reduce radiation dose or shorten
scanning time. However, such a reconstruction suffers from high noise and
severe artifacts due to the incompleteness of sinogram. To derive quality
reconstruction, previous state-of-the-art methods use UNet-like neural
architectures to directly predict the full view reconstruction from limited
view data; but these methods leave the deep network architecture issue largely
intact and cannot guarantee the consistency between the sinogram of the
reconstructed image and the acquired sinogram, leading to a non-ideal
reconstruction. In this work, we propose a novel recurrent reconstruction
framework that stacks the same block multiple times. The recurrent block
consists of a custom-designed residual dense spatial-channel attention network.
Further, we develop a sinogram consistency layer interleaved in our recurrent
framework in order to ensure that the sampled sinogram is consistent with the
sinogram of the intermediate outputs of the recurrent blocks. We evaluate our
methods on two datasets. Our experimental results on AAPM Low Dose CT Grand
Challenge datasets demonstrate that our algorithm achieves a consistent and
significant improvement over the existing state-of-the-art neural methods on
both limited angle reconstruction (over 5dB better in terms of PSNR) and sparse
view reconstruction (about 4dB better in term of PSNR). In addition, our
experimental results on Deep Lesion datasets demonstrate that our method is
able to generate high-quality reconstruction for 8 major lesion types.
