  Low dose computed tomography (LDCT) has attracted more and more attention in
routine clinical diagnosis assessment, therapy planning, etc., which can reduce
the dose of X-ray radiation to patients. However, the noise caused by low X-ray
exposure degrades the CT image quality and then affects clinical diagnosis
accuracy. In this paper, we train a transformer-based neural network to enhance
the final CT image quality. To be specific, we first decompose the noisy LDCT
image into two parts: high-frequency (HF) and low-frequency (LF) compositions.
Then, we extract content features (X_{L_c}) and latent texture features
(X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF
part. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with
three encoders and decoders to obtain well-refined HF texture features. After
that, we combine these well-refined HF texture features with the pre-extracted
X_{L_c} to encourage the restoration of high-quality LDCT images with the
assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT
dataset show that our method produces superior results and outperforms other
methods.
