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Low dose computed tomography is a mainstream for clinical applications.How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, thereare stronger noise and more artifacts which are obstacles for practicalapplications. In the last few years, convolution-based end-to-end deep learningmethods have been widely used for LDCT image denoising. Recently, transformerhas 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-decoderDilation net-work (TED-net) to enrich the family of LDCT denoising algorithms.The model is free of convolution blocks and consists of a symmetricencoder-decoder block with sole transformer. Our model is evaluated on theAAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformanceover the state-of-the-art denoising methods.

TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising