  Low dose computed tomography is a mainstream for clinical applications.
How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there
are stronger noise and more artifacts which are obstacles for practical
applications. In the last few years, convolution-based end-to-end deep learning
methods have been widely used for LDCT image denoising. Recently, transformer
has shown superior performance over convolution with more feature interactions.
Yet its ap-plications in LDCT denoising have not been fully cultivated. Here,
we propose a convolution-free T2T vision transformer-based Encoder-decoder
Dilation net-work (TED-net) to enrich the family of LDCT denoising algorithms.
The model is free of convolution blocks and consists of a symmetric
encoder-decoder block with sole transformer. Our model is evaluated on the
AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance
over the state-of-the-art denoising methods.
