  Sparse-view computed tomography (CT) is known as a widely used approach to
reduce radiation dose while accelerating imaging through lowered projection
views and correlated calculations. However, its severe imaging noise and
streaking artifacts turn out to be a major issue in the low dose protocol. In
this paper, we propose a dual-domain deep learning-based method that breaks
through the limitations of currently prevailing algorithms that merely process
single image slices. Since the scanned object usually contains a high degree of
spatial continuity, the obtained consecutive imaging slices embody rich
information that is largely unexplored. Therefore, we establish a cascade model
named LS-AAE which aims to tackle the above problem. In addition, in order to
adapt to the social trend of lightweight medical care, our model adopts the
inverted residual with linear bottleneck in the module design to make it mobile
and lightweight (reduce model parameters to one-eighth of its original) without
sacrificing its performance. In our experiments, sparse sampling is conducted
at intervals of 4{\deg}, 8{\deg} and 16{\deg}, which appears to be a
challenging sparsity that few scholars have attempted before. Nevertheless, our
method still exhibits its robustness and achieves the state-of-the-art
performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while
ensuring high model mobility. Particularly, it still exceeds other current
methods when the sampling rate is one-fourth of them, thereby demonstrating its
remarkable superiority.
