  This paper studies 3D low-dose computed tomography (CT) imaging. Although
various deep learning methods were developed in this context, typically they
focus on 2D images and perform denoising due to low-dose and deblurring for
super-resolution separately. Up to date, little work was done for simultaneous
in-plane denoising and through-plane deblurring, which is important to obtain
high-quality 3D CT images with lower radiation and faster imaging speed. For
this task, a straightforward method is to directly train an end-to-end 3D
network. However, it demands much more training data and expensive
computational costs. Here, we propose to link in-plane and through-plane
transformers for simultaneous in-plane denoising and through-plane deblurring,
termed as LIT-Former, which can efficiently synergize in-plane and
through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both
convolution and transformer networks. LIT-Former has two novel designs:
efficient multi-head self-attention modules (eMSM) and efficient convolutional
feedforward networks (eCFN). First, eMSM integrates in-plane 2D self-attention
and through-plane 1D self-attention to efficiently capture global interactions
of 3D self-attention, the core unit of transformer networks. Second, eCFN
integrates 2D convolution and 1D convolution to extract local information of 3D
convolution in the same fashion. As a result, the proposed LIT-Former synergize
these two subtasks, significantly reducing the computational complexity as
compared to 3D counterparts and enabling rapid convergence. Extensive
experimental results on simulated and clinical datasets demonstrate superior
performance over state-of-the-art models. The source code is made available at
https://github.com/hao1635/LIT-Former.
